Malta

Human processing of nonlocal syntactic dependencies requires engagement of limited working memory for encoding, maintenance, and retrieval. This process creates an evolutionary pressure for language to be structured in a way that keeps the subparts of a dependency closer to each other, an efficiency principle termed dependency locality. The current study proposes that such a dependency locality pressure can be modulated by the surprisal of the antecedent, defined as the first part of a dependency, due to strategic allocation of working memory. In particular, antecedents with novel and unpredictable information are prioritized for memory encoding, receiving more robust representation against memory interference and decay, and thus are more capable of handling longer dependency length. We examine this claim by examining dependency corpora of six languages (Danish, English, Italian, Mandarin, Russian, and Spanish), with word surprisal generated from GPT-3 language model. In support of our hypothesis, we find evidence for a positive correlation between dependency length and the antecedent surprisal in most of the languages in our analyses.

EACL 2024

Syntactic dependency length shaped by strategic memory allocation

corpus study

cross-linguistic

memory interference

strategic memory allocation

dependency locality

dependency length

human sentence processing

working memory

Join us for CogSci2022! Our 44th Annual Conference will be centered around the theme of diversity, with an accompanying range of keynotes and invited speakers who themselves highlight the diversity of our field. Our three symposia are Cognition across Diverse Populations, Cognition within Diverse Societies, and Global Challenges for Cognitive Science. The conference is being offered in a fully hybrid environment for onsite and online attendees.
 
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CogSci 2022

The 44th Annual Meeting of the Cognitive Science Society highlighted research on the theme Cognitive Diversity in addition to the full breadth and diversity of research topics offered by the society’s membership. 





workshop paper

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Over 7,000 of the world's 7,168 living languages are still low-resourced. This paper aims to narrow the language documentation gap by creating multiparallel dictionaries, clustered by SIL's semantic domains. This task is new for machine learning and has previously been done manually by native speakers. We propose GUIDE, a language-agnostic tool that uses a GNN to create and populate semantic domain dictionaries, using seed dictionaries and Bible translations as a parallel text corpus. Our work sets a new benchmark, achieving an exemplary average precision of 60% in eight languages without training data and predicting an average of 2,400 dictionary entries. We share the code, model, multilingual test set, and new dictionaries with the research community.

GUIDE: Creating Semantic Domain Dictionaries for Low-Resource Languages

Traditional dialectology or dialect geography is the study of geographical variation of language. Originated in Europe and pioneered in Germany and France, this field has predominantly been focusing on sounds, more specifically, on segments. Similarly, quantitative approaches to language variation concerned with the phonetic level are in most cases focusing on segments as well. However, more than half of the world’s languages include lexical tones (Yip, 2002). Despite this, tones are still underexplored in quantitative language comparison, partly due to the low accessibility of the suitable data. This paper aims to introduce a newly digitised dataset which comes from the Yue- and Pinghua-speaking areas in Southern China, with over 100 dialects. This dataset consists of two parts: tones and segments. In this paper, we illustrate how we can computationaly model tones in order to explore linguistic variation. We have applied a tone distance metric on our data, and we have found that 1) dialects also form a continuum on the tonal level and 2) other than tonemic (inventory) and tonetic differences, dialects can also differ in the lexical distribution of tones. The availability of this dataset will hopefully enable further exploration of the role of tones in quantitative typology and NLP research.

A New Dataset for Tonal and Segmental Dialectometry from the Yue- and Pinghua-Speaking Area

Closely related languages show linguistic similarities that allow speakers of one language to understand speakers of another language without having actively learned it. Mutual intelligibility varies in degree and is typically tested in psycholinguistic experiments. To study mutual intelligibility computationally, we propose a computer-assisted method using the Linear Discriminative Learner, a computational model developed to approximate the cognitive processes by which humans learn languages, which we expand with multilingual semantic vectors and multilingual sound classes. We test the model on German, Dutch, and English, three closely related Germanic languages. We find that our model’s comprehension accuracy depends on 1) the automatic trimming of inflections and 2)the language pair for which comprehension is tested. Our multilingual modelling approach does not only offer new methodological findings for automatic testing of mutual intelligibility across languages but also extends the use of Linear Discriminative Learning to multilingual settings.

A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages

Comparing eye-movement data of the readers of different languages or language varieties enables us to delve into the linkage between language variation and cognitive processing pattern. Within this scope, the Chinese language is particularly attractive in terms of the uniqueness of its written system, and the diversity of written varieties. As the native language of a wide spectrum of Chinese language speakers, Mandarin contrasts with Cantonese essentially on the complexity and semantic transparency of a single character, and also considerably on word length variability and word order flexibility. These manifold variations back a hypothesis that Simplified Mandarin readers exert more cognitive effort on the context that the Traditional Cantonese readers. The computational language models (LM) accurately quantify the predictive processing (e.g. Surprisal) and contextual effect (e.g. Entropy and Entropy Reduction) underlying the real-time language processing, which allows a deeper and reproducible inspection of high-level factors and their manifestation on the variation of language processing over different written scripts. Leveraging both masked and incremental language models, this study focus on the feature importance of different LM features (Surprisal, Entropy, and Entropy Reduction) in the modeling of eye-movement behaviors (durations and saccades in both early and late stages). The results suggest that the variation of eye-movement patterns is more salient on newly introduced LM features while vague on the widely studied properties. Specifically, Entropy effect is larger in Mandarin reading while Surprisal effect is more pronounced in Cantonese reading.

Predicting Mandarin and Cantonese Adult Speakers’ Eye-Movement Patterns in Natural Reading

LAJaR (Language Atlas of Japanese and Ryukyuan) is a linguistic typology database focusing on micro-variation of the Japonic (Japanese and Ryukyuan) languages. This paper aims to report the design and progress of this ongoing database project. Finally, we also show a case study utilizing its database on zero copulas among the Japonic languages.

Language Atlas of Japanese and Ryukyuan (LAJaR): A Linguistic Typology Database for Endangered Japonic Languages

This paper lays the groundwork for initiating research into Source Language Identification; the task of identifying the original language of a machine-translated text. We contribute a dataset of translations from a typologically diverse spectrum of languages into English and use it to set initial baselines for this novel task.

GTNC: A Many-To-One Dataset of Google Translations from NewsCrawl

Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression. The problem of processing multiple languages at once, each with their own variations of emotional expression, makes this task even more challenging for code-mixed data. Pre-trained language models (PLMs) have achieved high performance for many tasks and languages. It remains to be seen, however, whether these models learn and are robust to the differences in emotional expression across languages. Sociolinguistic studies have shown that Hinglish speakers make associations between language choice and emotion. Namely, they prefer Hindi when expressing negative emotions and English when expressing positive emotions. To understand if language models can pick up on these associations, we study the effect of language on emotion prediction across 3 PLMs on a Hinglish emotion classification dataset. Using LIME and token level language ID, we find that models do learn these associations between language choice and emotion. Moreover, having code-mixed data present in the pre-training can augment that learning even when task-specific data is scarce.

Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification

In order to draw generalizable conclusions about the performance of multilingual models across languages, it is important to evaluate on a set of languages that captures linguistic diversity. Linguistic typology is increasingly used to justify language selection, inspired by language sampling in linguistics. However, justifications for 'typological diversity' exhibit great variation, as there seems to be no set definition, methodology or consistent link to linguistic typology. In this work, we provide a systematic insight into how previous work in the ACL Anthology uses the term 'typological diversity'. Our two main findings are: 1) what is meant by typologically diverse language selection is not consistent and 2) the actual typological diversity of the language sets in these papers varies greatly. We argue that, when making claims about 'typological diversity', an operationalization of this should be included. A systematic approach that quantifies this claim, also with respect to the number of languages used, would be even better.

A Call for Consistency in Reporting Typological Diversity

In traditional studies on language evolution, scholars often emphasize the importance of sound laws and sound correspondences for phylogenetic inference of language family trees. However, to date, computational approaches have typically not taken this potential into account. Most computational studies still rely on lexical cognates as major data source for phylogenetic reconstruction in linguistics, although there do exist a few studies in which authors praise the benefits of comparing words at the level of sound sequences. Building on (a) ten diverse datasets from different language families, and (b) state-of-the-art methods for automated cognate and sound correspondence detection, we test, for the first time, the performance of sound-based versus cognate-based approaches to phylogenetic reconstruction. Our results show that phylogenies reconstructed from lexical cognates are topologically closer, by approximately one third with respect to the generalized quartet distance on average, to the gold standard phylogenies than phylogenies reconstructed from sound correspondences.

Are Sounds Sound for Phylogenetic Reconstruction?

In Universal Dependencies, compounds, which we understand as words containing two or more roots, are represented according to tokenization, which reflects the orthographic conventions of the language. A closed compound (e.g. water fall) corresponds to a single word in Universal Dependencies while a hyphenated compound (father-in-law) and an open compound (apple pie) to multiple words. The aim of this paper is to open a discussion on how to move towards a more consistent annotation of compounds. The solution we argue for is to represent the internal structure of all compound types analogously to syntactic phrases, which would not only increase the comparability of compounding within and across languages, but also allow comparisons of compounds and syntactic phrases.

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GUIDE: Creating Semantic Domain Dictionaries for Low-Resource Languages

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