Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks
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Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning
of Pragmatically Motivated Tasks
Jimin Sun1* Hwijeen Ahn2* Chan Young Park3*
Yulia Tsvetkov3 David R. Mortensen3
1
Seoul National University, Republic of Korea
2
Sogang University, Republic of Korea
3
Language Technologies Institute, Carnegie Mellon University, USA
jiminsun@dm.snu.ac.kr, hwijeen@sogang.ac.kr,
{chanyoun, ytsvetko, dmortens}@cs.cmu.edu
Abstract on semantic and pragmatic differences across lan-
guages.1 We devise a new distance measure be-
Much work in cross-lingual transfer learning tween languages based on linguistic proxies of cul-
explored how to select better transfer lan-
ture. We hypothesize that it can be used to se-
guages for multilingual tasks, primarily fo-
cusing on typological and genealogical simi- lect transfer languages and improve cross-lingual
larities between languages. We hypothesize transfer learning, specifically in pragmatically-
that these measures of linguistic proximity are motivated tasks such as sentiment analysis, since
not enough when working with pragmatically- expressions of subtle sentiment or emotion—such
motivated tasks, such as sentiment analysis. as subjective well-being (Smith et al., 2016), anger
As an alternative, we introduce three linguistic (Oster, 2019), or irony (Karoui et al., 2017)—have
features that capture cross-cultural similarities been shown to vary significantly by culture.
that manifest in linguistic patterns and quantify
distinct aspects of language pragmatics: lan-
We focus on three distinct aspects in the intersec-
guage context-level, figurative language, and tion of language and culture, and propose features
the lexification of emotion concepts. Our anal- to operationalize them. First, every language and
yses show that the proposed pragmatic features culture rely on different levels of context in com-
do capture cross-cultural similarities and align munication. Western European languages are gen-
well with existing work in sociolinguistics and erally considered low-context languages, whereas
linguistic anthropology. We further corrobo- Korean and Japanese are considered high-context
rate the effectiveness of pragmatically-driven
languages (Hall, 1989). Second, similar cultures
transfer in the downstream task of choosing
transfer languages for cross-lingual sentiment construct and construe figurative language simi-
analysis. larly (Casas and Campoy, 1995; Vulanović, 2014).
Finally, emotion semantics is similar between lan-
1 Introduction guages that are culturally-related (Jackson et al.,
2019). For example, in Persian, ‘grief’ and ‘regret’
Hofstede et al. (2005) defined culture as the col- are expressed with the same word whereas ‘grief’
lective mind which “distinguishes the members is co-lexified with ‘anxiety’ in Dargwa. There-
of one group of people from another.” Cultural fore, Persian speakers may perceive ‘grief’ as more
idiosyncrasies affect and shape people’s beliefs similar to ‘regret,’ while Dargwa speakers may as-
and behaviors. Linguists have particularly focused sociate the concept with ‘anxiety.’
on the relationship between culture and language, We validate the proposed features qualitatively,
revealing in qualitative case studies how cultural and also quantitatively by an extrinsic evaluation
differences are manifested as linguistic variations method. We first analyze each linguistic feature
(Siegel, 1977).
1
Quantifying cross-cultural similarities from lin- In linguistics, pragmatics has both a broad and a narrow
sense. Narrowly, the term refers to formal pragmatics. In the
guistic patterns has largely been unexplored in broad sense, which we employ in this paper, pragmatics refers
NLP, with the exception of studies that focused on to contextual factors in language use. We are particularly con-
cross-cultural differences in word usage (Garimella cerned with cross-cultural pragmatics and finding quantifiable
linguistic measures that correspond to aspects of cultural con-
et al., 2016; Lin et al., 2018). In this work, we text. These measures are not the cultural characteristics that
aim to quantify cross-cultural similarity, focusing would be identified by anthropological linguists themselves
but are rather intended to be measurable correlates of these
*The first three authors contributed equally. characteristics.
2403
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 2403–2414
April 19 - 23, 2021. ©2021 Association for Computational Linguisticsto confirm that they capture the intended cultural transfer language ltf , which leads to the following
patterns. We find that the results corroborate the definition of LCR:
existing work in sociolinguistics and linguistic an-
thropology. Next, as a practical application of our LCR-pron(ltf , ltg ) = ptr(ltg )/ptr(ltf )
features, we use them to rank transfer languages LCR-verb(ltf , ltg ) = vtr(ltg )/vtr(ltf )
for cross-lingual transfer learning. Lin et al. (2019)
have shown that selecting the right set of transfer Literal Translation Quality Similar cultures
languages with syntactic and semantic language- tend to share similar figurative expressions, includ-
level features can significantly boost the perfor- ing idiomatic multiword expressions (MWEs) and
mance of cross-lingual models. We incorporate our metaphors (Kövecses, 2003, 2010). For example,
features into Lin et al. (2019)’s ranking model to like father like son in English can be translated
evaluate the new cultural features’ utility in select- word-by-word into a similar idiom tel père tel fils
ing better transfer languages. Experimental results in French. However, in Japanese, a similar idiom
show that incorporating the features improves the 蛙の子は蛙 (Kaeru no ko wa kaeru) “A frog’s
performance for cross-lingual sentiment analysis, child is a frog.” cannot be literally translated.
but not for dependency parsing. These results sup- Literal translation quality (LTQ) feature quanti-
port our hypothesis that cultural features are more fies how well a given language pair’s MWEs are
helpful when the cross-lingual task is driven by preserved in literal (word-by-word) translation, us-
pragmatic knowledge. 2 ing a bilingual dictionary. A well-curated list of
MWEs is not available for the majority of lan-
2 Pragmatically-motivated Features guages. We thus follow an automatic extraction
We propose three language-level features that quan- approach of MWEs (Tsvetkov and Wintner, 2010).
tify the cultural similarities across languages. First, a variant of pointwise mutual information,
PMI3 (Daille, 1994) is used to extract noisy lists of
Language Context-level Ratio A language’s
top-scoring n-grams from two large monolingual
context-level reflects the extent to which the lan-
corpora from different domains, and intersecting
guage leaves the identity of entities and predi-
the lists filters out domain-specific n-grams and
cates to context. For example, an English sen-
retains the language-specific top-k MWEs. Then,
tence Did you eat lunch? explicitly indicates the
a bilingual dictionary between ltf and ltg and a
pronoun you, whereas the equivalent Korean sen-
parallel corpus between the pair are used. 3 For
tence ᄌᆷᄉ
ᅥ ᆷᄆ
ᅵ ᆨᄋ
ᅥ ᆻᄂ
ᅥ ᅵ? (= Did eat lunch?) omits the
each n-gram in ltg ’s MWEs, we search for its lit-
pronoun. Context-level is considered one of the
eral translations extracted using the dictionary in
distinctive attributes of a language’s pragmatics
parallel sentences containing the n-gram. For any
in linguistics and communication studies, and if
word in the n-gram, if there is a translation in the
two languages have similar levels of context, their
parallel sentence, we consider this as hit, otherwise
speakers are more likely to be from similar cultures hit
as miss. And we calculate hit ratio as (hit+miss)
(Nada et al., 2001).
for each n-gram found in the parallel corpus. Fi-
The language context-level ratio (LCR) feature
nally, we average the hit ratios of all n-grams and
approximates this linguistic quality. We compute
z-normalize over the transfer languages to obtain
the pronoun- and verb-token ratio, ptr(lk ) and
LTQ(ltf , ltg ).
vtr(lk ) for each language lk , using part-of-speech
tagging results. We first run language-specific POS- Emotion Semantics Distance Emotion seman-
taggers over a large mono-lingual corpus for each tic distance (ESD) measures how similarly emo-
language. Next, we compute ptr as the ratio of tions are lexicalized across languages. This is in-
count of pronouns in the corpus to the count of spired by Jackson et al. (2019) who used colexi-
all tokens. vtr is obtained likewise with verb to- fication patterns (i.e., when different concepts are
kens. Low ptr, vtr values may indicate that a expressed using the same lexical item) to capture
language leaves the identity of entities and predi- the semantic similarity of languages. However,
cates, respectively, to context. We then compare colexification patterns require human annotation,
these values between the target language ltg and 3
While dictionaries and parallel corpora are not available
2
Code and data are publicly available at https:// for many languages, they are easier to obtain than the task-
github.com/hwijeen/langrank. specific annotations of MWEs.
2404and existing annotations may not be comprehen-
sive. We extend Jackson et al. (2019)’s method by
using cross-lingual word embeddings.
We define ESD as the average distance of emo-
tion word vectors in transfer and target languages,
after aligning word embeddings into the same
space. More specifically, we use 24 emotion con-
cepts defined in Jackson et al. (2019) and use bilin-
gual dictionaries to expand each concept into ev-
ery other language (e.g., love and proud to Liebe
and stolz in German). We then remove the emo-
tion word pairs from the bilingual dictionaries, and
use the remaining pairs to align word embeddings
of source into the space of target languages. We
Figure 1: Plot of languages in ptr and vtr plane.
hypothesize that if words correspond to the same Languages are color-coded according to the cultural
emotion concept in different languages (e.g., proud areas defined in Siegel (1977).
and stolz) have similar meaning, they should be
aligned to the same point despite the lack of super-
vision. However, because each language possesses 3.2 LCR and Language Context-level
different emotion semantics, emotions are scattered ptr approximates how often discourse entities
into different positions. We thus define ESD as the are indexed with pronouns rather than left conjec-
average cosine distance between languages: turable from context. Similarly, vtr estimates the
X rate at which predicates appear explicitly as verbs.
ESD(ltf , ltg ) = cos(vtf,e , vtg,e )/|E| In order to examine to which extent these features
e∈E
reflect context-levels, we plot languages on a two-
where E is the set of emotion concepts and vtf,e is dimensional plane where the x-axis indicates ptr
the aligned emotion word vector of language ltf . and the y-axis indicates vtr in Figure 1.
The plot reveals a clear pattern of context-levels
3 Feature Analysis in different languages. Low-context languages
such as German and English (Hall, 1989) possess
In this section, we evaluate the proposed
the largest values of ptr. On the other extreme are
pragmatically-motivated features intrinsically.
located Korean and Japanese with low ptr, which
Throughout the analyses, we use 16 languages
are representative of high-context languages. One
listed in Figure 4 which are later used for extrinsic
thing to notice is the isolated location of Turkish
evaluation (§5).
with a high vtr. This is morphosyntactically plau-
3.1 Implementation Details sible as a lot of information is expressed by the
affixation to verbs in Turkish.
We used multilingual word tokenizers from NLTK
and RDR POS Tagger (Nguyen et al., 2014) for 3.3 LTQ and MWEs
most of the languages except for Arabic, Chinese,
Japanese, and Korean, where we used PyArabic, LTQ uses n-grams with high PMI scores as prox-
Jieba, Kytea, and Mecab, respectively. For mono- ies for figurative language MWE (PMI MWEs).
lingual corpora, we used the news-crawl 1M cor- We evaluate the quality of selected MWEs and the
pora from Leipzig (Goldhahn et al., 2012) for both resulting LTQ by comparing with human-curated
LCR and LTQ. We used bilingual dictionaries from list of figurative language MWE (gold MWEs)
Choe et al. (2020) and TED talks corpora (Qi et al., that are available in some languages. We col-
2018) for both parallel corpora and an additional lected gold MWEs in multiple languages from
monolingual corpus for LTQ. We focused on bi- Wiktionary4 . We discarded languages with less
grams and trigrams and set k, the number of ex- than 2,000 phrases on the list, resulting in four
tracted MWEs, to 500. We followed Lample et al. languages (English, French, German, Spanish) for
(2018) to generate the supervised cross-lingual 4
For example, https://en.wiktionary.org/
word embeddings for ESD. wiki/Category:English_idioms
2405(a) Network based on Emotion Semantics Distance. (b) Network based on syntactic distance.
Figure 2: Network of languages color-coded by their cultural areas. An edge is added between the two languages
if a language is ranked in the top-2 closest languages of the other language in terms of feature value.
analysis. good and love in the Nakh-Daghestanian language
First, we check how many PMI MWEs are ac- family. Our results derived from ESD do not rely
tually in the gold MWEs. Out of the top-500 PMI on colexification patterns, but also support this find-
bigrams and trigrams, 19.0% of bigrams and 3.8% ing. The nearest neighbors of the Chinese word for
of trigrams are included in the gold MWE list (av- hope was want and pity, while they were found as
eraged over four languages). For example, the tri- love and joy for hope in Arabic.
grams in the PMI MWEs, keep an eye and take into In Figure 2, we compare ESD to the syntactic
account, are considered to be in the gold MWEs distance between languages by constructing two
as keep an eye peeled and take into account are in networks of languages based on each feature. Fig-
the list. The seemingly low percentages are reason- ure 2a uses ESD as reference while Figure 2b uses
able, regarding that the PMI scores are designed to the syntactic distance from the URIEL database
extract collocations patterns rather than figurative (Littell et al., 2017). Each node represents a lan-
languages themselves. guage, color-coded by its cultural area. For each
Secondly, to validate using PMI MWEs as prox- language, we sort the other languages according to
ies, we compare the LTQ of PMI MWEs with the the distance value. When a language is in the list of
LTQ using gold MWEs. Specifically, we obtained top-k closest languages, we draw an edge between
the LTQ scores of each language pair with target the two. We set k = 2.
languages limited to the four European languages We see that languages in the same cultural areas
mentioned above. Then for each target language, tend to form more cohesive clusters in Figure 2a
we measured Pearson correlation coefficient be- compared to Figure 2b. The portion of edges within
tween the two LTQ scores based on the two MWE the cultural areas is 76% for ESD while it is 59%
lists. The average coefficient was 0.92, which indi- for syntactic distance. These results indicate that
cates a strong correlation between the two resulting ESD effectively extracts linguistic information that
LTQ scores, and thus justifies using PMI MWEs aligns well with the commonly shared perception
for all other languages. of cultural areas.
3.4 ESD and Cultural Grouping 3.5 Correlation with Geographical Distance
We investigate what is carried by ESD by visualiz- Regarding the language clusters in Figure 2a, some
ing and looking at the nearest neighbors of emotion may suspect that geographic distance can substitute
vectors.5 Jackson et al. (2019) used word colex- the pragmatically-inspired features. For Chinese,
ification patterns to reveal that the same emotion Korean and Japanese are the closest languages by
concepts cluster with different emotions according ESD, which can also be explained by their geo-
to the language family they belong to. For instance, graphical proximity. Do our features add additional
in Tai-Kadai languages, hope appears in the same pragmatic information, or can they simply be re-
cluster as want and pity, while hope associates with placed by geographical distance?
5
A visualization demo of emotion vectors can be found at To verify this speculation, we evaluate Pearson’s
https://bit.ly/emotion_vecs. correlation coefficient of each pragmatic feature
2406value with geographical distance from URIEL. The
feature with the strongest correlation was ESD
(r=0.4). The least correlated was LCR-verb
(r=0.03), followed by LCR-pron (r=0.17) and
Ranking
LTQ (r=−0.31)6 . The results suggest that the
Model
pragmatic features contain extra information that
cannot be subsumed by geographic distance.
Figure 3: Illustration of transfer language ranking
4 Extrinsic Evaluation: Ranking problem when the target language is French (fr) and
Transfer Languages there are three available transfer languages: Arabic
(ar), Russian (ru), and Chinese (zh). The output
To demonstrate the utility of our features, we ap- ranking r̂ fr is compared to the ground truth ranking r fr
ply them to a transfer language ranking task for which is determined by the zero-shot performance z
cross-lingual transfer learning. We first present the of cross-lingual models.
overall task setting, including the datasets and mod-
els used for the two cross-lingual tasks. Next, we West Europe: East Europe:
describe the transfer language ranking model and Dutch (1089) Czech (54540)
its evaluation metrics. English (1472) Polish (26284)
French (20771) Russian (2289)
German (56333)
4.1 Task Setting Spanish (1396) Middle East:
We define our task as the language ranking prob- Arabic (4111)
lem: given the target language ltg , we want East Asia: Persian (3904)
Turkish (907)
to rank a set of n candidate transfer languages Chinese (2333)
(1) (n) Japanese (21095)
Ltf ={ltf , . . . , ltf } by their usefulness when Korean (18000)
South Asia:
transferred to ltg , which we refer to as transfer- Hindi (2707)
Tamil (417)
ability (illustrated in Figure 3). The effectiveness
of cross-lingual transfer is often measured by eval-
Figure 4: Languages used throughout the experiments
uating the joint training or zero-shot transfer per-
are grouped by their cultural areas (Siegel, 1977). The
formance (Wu and Dredze, 2019; Schuster et al., numbers indicate the size of each dataset.
2019). In this work, we quantify the effectiveness
as the zero-shot transfer performance, following
Lin et al. (2019). Our goal is to train a model that few. The ranking model takes f tf,tg of every ltf as
ranks available transfer languages in Ltf by their input, and predicts the transferability ranking rbtg .
transferability for a target language ltg . Using r tg from the previous step as training data,
To train the ranking model, we first need to find the model learns to find optimal transfer languages
the ground-truth transferability rankings, which based on f tf,tg . The trained model can either be
operate as the model’s training data. We evaluate used to select the optimal set of transfer languages,
the zero-shot performance ztf,tg by training a task- or to decide which language to additionally anno-
specific cross-lingual model solely with transfer tate during the data creation process.
language ltf and testing on ltg . After evaluating
4.2 Task & Dataset
ztf,tg for each candidate transfer language in Ltf ,
we obtain the optimal ranking of languages r tg by We apply the proposed features to train a rank-
sorting languages according to the measured ztf,tg . ing model for two distinctive tasks: multilingual
Note that r tg also depends on downstream task. sentiment analysis (SA) and multilingual depen-
Next, we train the language ranking model. The dency parsing (DEP). The tasks are chosen based
ranking model predicts the transfer ranking of can- on our hypothesis that high-order information such
didate languages. Each source, target pair (ltf , ltg ) as pragmatics would assist sentiment analysis while
is represented as a vector of language features it may be less significant for dependency parsing,
f tf,tg , which may include phonological similar- where lower-order information such as syntax is
ity, typological similarity, word-overlap to name a relatively stressed.
6
When two languages are more similar, LTQ is higher SA As there is no single sentiment analysis
whereas geographic distance is smaller. dataset covering a wide variety of languages, we
2407collected various review datasets from different relevant languages for computing MAP are defined
sources.7 All samples are labeled as either posi- as the top-k languages in terms of zero-shot perfor-
tive or negative. In case of datasets rated with a mance in the downstream task. In our experiments,
five-point Likert scale, we mapped 1–2 to negative we set k to 3 for MAP. Similarly, we use NDCG@3.
and 4–5 to positive. We settled on a dataset consist We train and evaluate the model using leave-one-
of 16 languages categorized into five distinct cul- out cross-validation: where one language is set
tural groups: West Europe, East Europe, East Asia, aside as the test language while other languages
South Asia, and Middle East (Figure 4). are used to train the ranking model. Among the
training languages, each language is posited in turn
DEP To compare the effectiveness of the pro-
as the target language while others are the transfer
posed features on syntax-focused tasks, we chose
languages.
datasets of the same set of 16 languages from Uni-
versal Dependencies v2.2 (Nivre et al., 2018). 5 Experiments
4.3 Task-Specific Cross-Lingual Models 5.1 Baselines
SA Multilingual BERT (mBERT) (Devlin et al., L ANG R ANK L ANG R ANK (Lin et al., 2019) uses
2019), a multilingual extension of BERT pretrained 13 features to train the ranking model: The dataset
with 104 different languages, has shown strong size in transfer language (tf size), target lan-
results in various text classification tasks in cross- guage (tg size), and the ratio between the two
lingual settings (Sun et al., 2019; Xu et al., 2019; (ratio size); Type-token-ratio (ttr) which
Li et al., 2019). We use mBERT to conduct zero- measures lexical diversity and word overlap
shot cross-lingual transfer and to extract optimal for lexical similarity between a pair of languages;
transfer language rankings: fine-tune mBERT on various distances between a language pair from the
transfer language data and test it on target language URIEL database (geographic geo, genetic gen,
data. The performance is measured by the macro inventory inv, syntactic syn, phonological phon
F1 score on the test set. and featural feat).
DEP We adopt the setting from Ahmad et al. MTV EC Malaviya et al. (2017) proposed to
(2018) to perform cross-lingual zero-shot transfer. learn a language representation while training a
We train deep biaffine attentional graph-based mod- neural machine translation (NMT) system in a sim-
els (Dozat and Manning, 2016) which achieved liar fashion to Johnson et al. (2017). During train-
state-of-the-art performance in dependency parsing ing, a language token is prepended to the source
for many languages. The performance is evaluated sentence and the learned token’s embedding be-
using labeled attachment scores (LAS). comes the language vector. Bjerva et al. (2019)
has shown that such language representations con-
4.4 Ranking Model & Evaluation tain various types of linguistic information ranging
Ranking Model For the language ranking model, from word order to typological information. We
we employ gradient boosted decision trees, Light- used the one released by Malaviya et al. (2017)
GBM (Ke et al., 2017), which is one of the state- which has the dimension of 512.
of-the-art models for ranking tasks.8
5.2 Individual Feature Contribution
Ranking Evaluation Metric We evaluate the We first look into whether the proposed features are
ranking models’ performance with two standard helpful in ranking transfer languages for sentiment
metrics for ranking tasks: Mean Average Preci- analysis and dependency parsing (Table 1). We
sion (MAP) and Normalized Discounted Cumula- add all three features (P RAG) to the two baseline
tive Gain at position p (NDCG@p) (Järvelin and features (L ANG R ANK, MTV EC) and compare the
Kekäläinen, 2002). While MAP assumes a binary performance in the two tasks. Results show that our
concept of relevance, NDCG is a more fine-grained features improve both baselines in SA, implying
measure that reflects the ranking positions. The that the pragmatic information captured by our fea-
7
Details are provided in Appendix A. Note that the differ- tures is helpful for discerning the subtle differences
ence in domain and label distribution of data can also affect in sentiment among languages.
the transferability, and a related discussion is in §5.4
8
More details on the cross-lingual models, ranking model, In the case of DEP, including our features brings
and their training can be found in Appendix B. inconsistent results to performance. The features
2408SA DEP SA DEP
MAP NDCG MAP NDCG MAP NDCG MAP NDCG
L ANG R ANK 71.3 86.5 63.0 82.2 Pretrain-specific 39.0 55.5 - -
L ANG R ANK +P RAG 76.0 90.9 61.7 80.5 Data-specific 68.0 85.4 37.2 55.0
- LCR 75.0 88.3 60.3 79.6 Typology 44.9 60.7 58.0 79.8
- LTQ 72.4 89.3 63.1* 81.3* Geography 24.9 55.0 32.3 65.1
- ESD 77.7* 92.1* 58.2 78.5 Orthography 34.2 56.6 35.5 60.5
MTV EC 71.1 89.5 43.0 69.7 Pragmatic 73.0 88.0 46.5 71.8
MTV EC +P RAG 74.3 90.8 49.7 74.8
- LCR 72.9 90.1 54.1* 76.3* Table 2: Ranking performance using each feature
- LTQ 71.2 89.0 53.0* 78.6* group as input to the ranking model.
- ESD 73.1 90.7 45.3 73.9
Table 1: Evaluation results of our features (P RAG)
added to each baseline. The higher scores are further investigate to what extent each kind of in-
boldfaced. Rows in gray indicate ablation studies. formation is useful to each task by conducting
* is marked when improvements are made compared group-wise comparisons. To this end, we group
to L ANG R ANK +P RAG, MTV EC +P RAG respectively. the features into five categories: Pretrain-specific,
Data-specific, Typology, Geography, Orthography,
help the performance of MTV EC while they deteri- and Pragmatic. Pretrain-specific features cover fac-
orate the performance of L ANG R ANK. Although tors that may be related to the performance of pre-
some performance increase was observed when ap- trained language models used in our task-specific
plied to MTV EC, the performance of MTV EC in cross-lingual models. Specifically, we used the size
DEP remains extremely poor. These conflicting of the Wikipedia training corpus of each language
trends suggest that pragmatic information is not used in training mBERT.9 Note that we do not mea-
crucial to less pragmatically-driven tasks, repre- sure this feature group’s performance on DEP as no
sented as dependency parsing in our case. pretrained language model was used in DEP. Data-
The low performance of MTV EC in DEP is no- specific features include tf size, tg size, and
ticeable as MTV EC is generally believed to con- ratio size. Typological features include geo,
tain a significant amount of syntactic information, syn, feat, phon, and inv distances. Geography
with much higher dimensionality than L ANG R ANK. includes geo distance in isolation. Orthographic
It also suggests the limitation of using distribu- feature is the word overlap between languages.
tional representations as language features; their Finally, the Pragmatic group consists of ttr and
lack of interpretability makes it difficult to control the three proposed features, LCR, LTQ, and ESD.
the kinds of information used in a model. ttr is included in Pragmatic as Richards (1987)
We additionally conduct ablation studies by re- have suggested that it encodes a significant amount
moving each feature from the +P RAG models to ex- of cultural information.
amine each feature’s contribution. The SA results Table 2 reports the performance of ranking mod-
show that LCR and LTQ significantly contribute els trained with the respective feature category.
to overall improvements achieved by adding our Interestingly, the two tasks showed significantly
features, while ESD turns out to be less helpful. different results; the Pragmatic group showed the
Sometimes, removing ESD resulted in a better per- best performance in SA while the Typology group
formance. In contrast, the results of DEP show that outperformed all other groups in DEP. This again
ESD consistently made a significant contribution, confirms that the features indicating cross-lingual
and LCR and LTQ were not useful. The results transferability differ depending on the target task.
imply that the emotion semantics information of Although the Pretrain-specific features were more
languages is surprisingly not useful in sentiment predictive than the Geography and Orthography
analysis, but more so in dependency parsing. features it was not as helpful as the Pragmatic fea-
tures.
5.3 Group-wise Contribution
The previous experiment suggests that the same
pragmatic information can be helpful to different 9
https://meta.wikimedia.org/wiki/List_
extents depending on the downstream task. We of_Wikipedias
24095.4 Controlling for Dataset Size ilarity by comparing the nearest neighborhood of
The performance of cross-lingual transfer depends words in different languages, showing that words in
not only on the cultural similarity between trans- some domains (e.g., time, quantity) exhibit higher
fer and target languages but also on other factors, cross-lingual alignment than other domains (e.g.,
including dataset size and label distributions. Al- politics, food, emotions). Jackson et al. (2019) rep-
though our model already accounts for the dataset resented each language as a network of emotion
size to some extent by including tf size as input, concepts derived from their colexification patterns
we conduct a more rigorous experiment to better and measured the similarity between networks.
understand the importance of cultural similarity in Auxiliary Language Selection in Cross-lingual
language selection. Specifically, we control the tasks There has been active work on leverag-
data size by down-sampling all SA data to match ing multiple languages to improve cross-lingual
both the size and label distribution of the second systems (Neubig and Hu, 2018; Ammar et al.,
smallest Turkish dataset.10 We then trained two 2016). Adapting auxiliary language datasets to
ranking models equipped with different sets of fea- the target language task can be practiced through
tures: L ANG R ANK and L ANG R ANK +P RAG. either language-selection or data-selection. Previ-
In terms of languages, we focus on a setting ous work on language-selection mostly relied on
where Turkish is the target and Arabic, Japanese leveraging syntactic or semantic resemblance be-
and Korean are the transfer languages. This is a tween languages (e.g. ngram overlap) to choose the
particularly interesting set of languages because the best transfer languages (Zoph et al., 2016; Wang
source languages are similar/dissimilar to Turkish and Neubig, 2019). Our approach extends this line
in different aspects; Korean and Japanese are typo- of work by leveraging cross-cultural pragmatics,
logically similar to Turkish, yet in cultural terms, an aspect that has been unexplored by prior work.
Arabic is more similar to Turkish.
In this controlled setting, the ground-truth rank- 7 Future Directions
ing reveals that the optimal transfer language
among the three is Arabic, followed by Korean and Typology of Cross-cultural Pragmatics The
Japanese. It indicates the important role of cultural features proposed here provide three dimensions in
resemblance in sentiment analysis which encapsu- a provisional quantitative cross-linguistic typology
lates the rich historical relationship shared between of pragmatics in language. Having been validated,
Arabic- and Turkish-speaking communities. L AN - both intrinsically and extrinsically, they can be used
G R ANK +P RAG chose Arabic as the best transfer in studies as a stand-in for cross-cultural similarity.
language, suggesting that the imposed cultural sim- They also open a new avenue of research, raising
ilarity information from the features helped the questions about what other quantitative features of
ranking model learn the cultural tie between the language are correlates of cultural and pragmatic
two languages. On the other hand, L ANG R ANK difference.
ranked Japanese the highest over Arabic, possibly
Model Probing Fine-tuning pretrained models
because the provided features mainly focus on ty-
to downstream tasks has become the de facto stan-
pological similarity over cultural similarity.
dard in various NLP tasks, and their success has
6 Related Work promoted the development of their multilingual ex-
tensions (Devlin et al., 2019; Lample and Conneau,
Quantifying Cross-cultural Similarity A few 2019). While the performance gains from these
recent work in psycholinguistics and NLP have models are undeniable, their learning dynamics re-
aimed to measure cultural differences, mainly from main obscure. This issue has prompted various
word-level semantics. Lin et al. (2018) suggested probing methods designed to test what kind of lin-
a cross-lingual word alignment method that pre- guistic information the models retain, including
serves the cultural, social context of words. They syntactic and semantic knowledge (Conneau et al.,
derive cross-cultural similarity from the embed- 2018; Liu et al., 2019; Ravishankar et al., 2019;
dings of a bilingual lexicon in the shared represen- Tenney et al., 2019). Similarly, our features can
tation space. Thompson et al. (2018) computed sim- be employed as a touchstone to evaluate a model’s
10
The size of the smallest language (Tamil; 417 samples) knowledge in cross-cultural pragmatics. Investi-
was too small to train an effective model. gating how different pretraining tasks affect the
2410learning of pragmatic knowledge will also be an Alexis Conneau, German Kruszewski, Guillaume Lam-
interesting direction of research. ple, Loı̈c Barrault, and Marco Baroni. 2018. What
you can cram into a single $&!#* vector: Probing
8 Conclusion sentence embeddings for linguistic properties. In
Proceedings of the 56th Annual Meeting of the As-
In this work, we propose three pragmatically- sociation for Computational Linguistics (Volume 1:
Long Papers), pages 2126–2136, Melbourne, Aus-
inspired features that capture cross-cultural sim- tralia. Association for Computational Linguistics.
ilarities that arise as linguistic patterns: language
context-level ratio, literal translation quality, and Béatrice Daille. 1994. Approche mixte pour
l’extraction automatique de terminologie: statis-
emotion semantic distance. Through feature analy-
tiques lexicales et filtres linguistiques. Ph.D. thesis,
ses, we examine whether our features can operate Ph. D. thesis, Université Paris 7.
as valid proxies of cross-cultural similarity. From a
practical standpoint, the experimental results show Jacob Devlin, Ming-Wei Chang, Kenton Lee, and
Kristina Toutanova. 2019. BERT: Pre-training of
that our features can help select the best transfer deep bidirectional transformers for language under-
language for cross-lingual transfer in pragmatically- standing. In Proceedings of the 2019 Conference
driven tasks, such as sentiment analysis. of the North American Chapter of the Association
for Computational Linguistics: Human Language
Acknowledgements Technologies, Volume 1 (Long and Short Papers),
pages 4171–4186, Minneapolis, Minnesota. Associ-
The authors are grateful to the anonymous review- ation for Computational Linguistics.
ers for their invaluable feedback. This material is Timothy Dozat and Christopher D. Manning. 2016.
based upon work supported by the National Sci- Deep biaffine attention for neural dependency pars-
ence Foundation under Grant No. IIS2007960. We ing. CoRR, abs/1611.01734.
would also like to thank Amazon for providing Aparna Garimella, Rada Mihalcea, and James Pen-
AWS credits. nebaker. 2016. Identifying cross-cultural differ-
ences in word usage. In Proceedings of COLING
2016, the 26th International Conference on Compu-
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2413A Dataset for Sentiment Analysis
Dataset Languages Domain Size POS/NEG
Chinese electronics 2333 1.53
Arabic hotel 4111 1.54
English restaurant 1472 2.14
SemEval-2016 Aspect Based
Dutch restaurant 1089 1.43
Sentiment Analysis
Spanish restaurant 1396 2.82
Russian restaurant 2289 3.81
Turkish restaurant 907 1.32
SentiPers Persian product 3904 1.8
French product 20771 8.0
Amazon Customer Reviews German product 56333 6.56
Japanese product 21095 8.05
CSFD CZ Czech movie 54540 1.04
Naver Sentiment Movie Corpus Korean movie 18000 1.0
Tamil Movie Review Dataset Tamil movie 417 0.48
PolEval 2017 Polish product 26284 1.38
Aspect based Sentiment Analysis Hindi product 2707 3.22
Table 3: Datasets for sentiment analysis.
B Task-Specific Models Details
SA Cross-lingual Model We performed super-
vised fine-tuning of multilingual BERT (mBERT)
(Devlin et al., 2019) for the sentiment analysis
task, as the model showed strong results in vari-
ous text classification tasks in cross-lingual settings
(Sun et al., 2019; Xu et al., 2019; Li et al., 2019).
mBERT is pretrained with 104 different languages,
including the 16 languages we used throughout our
experiment. We used a concatenation of mean and
max pooled representations from mBERT’s penulti-
mate layer, as it outperformed the standard practice
of using the last layer’s [CLS] token. The repre-
sentation was passed to a fully connected layer for
prediction. To extract optimal transfer rankings,
we conducted zero-shot transfer with mBERT: fine-
tuned mBERT on transfer language data and tested
it on target language data.
Ranking Model We used LightGBM (Ke et al.,
2017) with LambdaRank (Burges et al., 2007) al-
gorithm. The model consists of 100 decision trees
with 16 leaves each, and it was trained with the
learning rate of 0.1. We optimized NDCG to train
the model (Järvelin and Kekäläinen, 2002).
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