Papers by Antonios Anastasopoulos
LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages (2023.emnlp-main)
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| Challenge: | Currently, existing systems cannot accurately identify most of the world's 7000 languages due to lack of data and computational challenges. |
| Approach: | They propose a misprediction-resolution hierarchical model, LIMIT, that reduces error by 55% on a children's stories dataset and by 40% on 'fLORES-200' benchmark. |
| Outcome: | The proposed model reduces error by 55% on the MCS-350 and 40% on the FLORES-200 benchmarks. |
Dialect Normalization using Large Language Models and Morphological Rules (2025.findings-acl)
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| Challenge: | Natural language understanding systems struggle with low-resource languages, including many dialects of high-resourced ones. |
| Approach: | They propose a method that combines rule-based linguistically informed transformations and large language models with targeted few-shot prompting without any parallel data. |
| Outcome: | The proposed method is able to transform dialectal text into a standard variety while maintaining as much of the original meaning as possible. |
Script-Agnosticism and its Impact on Language Identification for Dravidian Languages (2025.naacl-long)
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| Challenge: | a recent study shows that modern systems are script-dependent in language identification (langID) many languages are written in multiple writing systems, and script diversity is common in low-resource languages. |
| Approach: | They propose to learn script-agnostic representations using different strategies . they use word-level script randomization and script exposure to a language written in multiple scripts . |
| Outcome: | The proposed methods exploit script randomization and exposure to a language written in multiple scripts to improve language identification while maintaining competitive performance on naturally occurring text. |
Predicting Performance for Natural Language Processing Tasks (2020.acl-main)
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| Challenge: | Natural language processing (NLP) is a vast field, with a wide variety of tasks, languages, and domains. |
| Approach: | They build regression models to predict evaluation score of an NLP experiment . they find that their models can produce meaningful predictions over unseen languages . |
| Outcome: | The proposed model outperforms baseline models and human experts on 9 different tasks. |
VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models (2026.acl-long)
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| Challenge: | Existing studies on VLM bias focus on portrait-style images and gender-occupation associations . existing studies ignore broader and more complex social stereotypes and their implied harm . |
| Approach: | They propose a large-scale VQA benchmark for evaluating bias in vision-language models . they use a question-answering framework that spans factuality, perception, stereotyping, and decision making . |
| Outcome: | The proposed framework examines bias in vision-language models using 30M+ images . findings reveal subtle, multifaceted, and surprising stereotypical patterns . |
When is Wall a Pared and when a Muro?: Extracting Rules Governing Lexical Selection (2021.emnlp-main)
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| Challenge: | Using manual content to learn languages is expensive and time consuming. |
| Approach: | They propose a method for automatically identifying fine-grained lexical distinctions and extracting rules explaining them in a human- and machine-readable format. |
| Outcome: | The proposed method is able to identify fine-grained distinctions and explain them in a human- and machine-readable format. |
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages (2022.aacl-main)
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| Challenge: | Large pretrained multilingual models have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. |
| Approach: | They propose to use language phylogenetic information to improve cross-lingual transfer by leveraging closely related languages in a structured, linguistically-informed manner. |
| Outcome: | The proposed model significantly improves on the baseline model on languages unseen during training. |
Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource (C18-1)
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Antonios Anastasopoulos, Marika Lekakou, Josep Quer, Eleni Zimianiti, Justin DeBenedetto, David Chiang
| Challenge: | a recent study examines POS tagging techniques on endangered languages . most natural language processing applications have been tested on only a handful of languages - a problem that is compounded by the lack of standard orthography. |
| Approach: | They evaluate POS tagging techniques on an endangered language, Griko . they use a semi-supervised method with cross-lingual transfer to achieve better accuracy . |
| Outcome: | The proposed method achieves 72.9% accuracy on a sample of 114 narratives in a language . the proposed method improves by 21 percentage points over previous methods . |
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing (2024.findings-emnlp)
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| Challenge: | Existing approaches to sign language translation use gloss annotations as an intermediary . a new approach to use large language models and word embeddings to improve Gloss2Text translation is needed. |
| Approach: | They propose to leverage large language models pre-trained on expansive and diverse corpora to improve Gloss2Text translation stage by using data augmentation and label-smoothing loss function. |
| Outcome: | The proposed approach surpasses state-of-the-art methods on the PHOENIX Weather 2014T dataset . it shows that gloss annotations can be used to guide the translation process . |
A Resource for Studying Chatino Verbal Morphology (2020.lrec-1)
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| Challenge: | a recent study focused on the verbal inflectional morphology of a mesoamerican language, San Juan Quiahije Chatino . low-resource endangered languages are left behind, while high-resourced ones are left out . a new approach to document endangered languages is needed to ensure language use continues . |
| Approach: | They present a resource focusing on the verbal inflectional morphology of a mesoamerican language . they use morphological inflection tables and baseline results to assess the language's morphologies . |
| Outcome: | The proposed resource focuses on the verbal inflectional morphology of a mesoamerican language . the authors show that the results are consistent with the current state of the art in the field . |
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (2020.emnlp-main)
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| Challenge: | Language models (LMs) capture factual knowledge by filling in the blanks of cloze-style prompts. |
| Approach: | They propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge and verify its effectiveness on several benchmark languages. |
| Outcome: | The proposed method improves the ability of multilingual LMs to access knowledge and verify its effectiveness on several benchmark languages. |
Birdie: Advancing State Space Language Modeling with Dynamic Mixtures of Training Objectives (2024.emnlp-main)
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| Challenge: | Efficient state space models struggle with tasks requiring in-context retrieval, such as text copying and associative recall, limiting their usefulness in practical settings. |
| Approach: | They propose a training procedure that improves the performance of SSMs on retrieval-intensive tasks such as phone book lookup, long paragraph question-answering, and infilling tasks. |
| Outcome: | The proposed training procedure improves performance on retrieval-intensive tasks that challenge current SSMs, such as phone book lookup, long paragraph question-answering, and infilling tasks. |
Tracing L1 Interference in English Learner Writing: A Longitudinal Corpus with Error Annotations (2025.emnlp-main)
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Poorvi Acharya, J. Elizabeth Liebl, Dhiman Goswami, Kai North, Marcos Zampieri, Antonios Anastasopoulos
| Challenge: | high-quality learner corpora are rarely available for studies of second language acquisition and language transfer. |
| Approach: | They propose to curate a corpus of adult learners with longitudinal data that includes 15 different L1s. |
| Outcome: | The proposed corpus contains 687 texts written by adult learners in the USA . authors show that the corpus can be used to explore language learning trajectories over time. |
It’s not a Non-Issue: Negation as a Source of Error in Machine Translation (2020.findings-emnlp)
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| Challenge: | In this study, we focus on negation, a universal, core property of human language that affects the semantics of an utterance. |
| Approach: | They focus on negation, a universal, core property of human language that affects semantics of an utterance. |
| Outcome: | The proposed method improves translation quality by 60% in some cases . the authors also provide a linguistically motivated analysis that directly explains the majority of the results. |
Talent or Luck? Evaluating Attribution Bias in Large Language Models (2026.findings-acl)
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| Challenge: | Existing studies on social biases in large language models focus on surface-level associations or isolated stereotypes. |
| Approach: | They propose a cognitively grounded bias evaluation framework to capture demographic biases across three contexts: single-actor, actor–actor and actor–observer. |
| Outcome: | The proposed framework captures comparative and perspective-driven biases overlooked in previous work. |
Machine Translation into Low-resource Language Varieties (2021.acl-short)
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| Challenge: | Current machine translation systems generate a "standard" target language, but many languages have multiple varieties that are different from the standard language. |
| Approach: | They propose a framework to rapidly adapt machine translation systems to generate different target varieties . they propose to use no parallel data to generate languages close to, but different from, the standard target language . |
| Outcome: | The proposed model improves on a system that generates Ukrainian and Belarusian in two languages with no parallel data. |
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba (2023.acl-long)
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| Challenge: | Bemba is the most populous language of Zambia but lacks resources for research . despite its significance, Bemba remains under-resourced and lacking in high-quality data and resources for NLP experiments and language technologies. |
| Approach: | They propose a large multimodal dataset for Bemba that includes images, transcriptions and translations. |
| Outcome: | The proposed dataset is based on images, transcriptions and translations of Bemba speakers . it provides baselines on speech recognition, machine translation and speech translation tasks . |
Reducing Confusion in Active Learning for Part-Of-Speech Tagging (2021.tacl-1)
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| Challenge: | Existing algorithms for annotating parts of speech are not optimal for all languages. |
| Approach: | They propose to use a data selection algorithm to select useful training samples to minimize annotation cost. |
| Outcome: | The proposed strategy outperforms existing strategies on six typologically diverse languages. |
Automatic Extraction of Rules Governing Morphological Agreement (2020.emnlp-main)
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Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, Graham Neubig
| Challenge: | Creating a descriptive grammar is an indispensable step for language documentation but it is tedious and time-consuming. |
| Approach: | They propose a framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. |
| Outcome: | The proposed framework extracts a grammatical specification that is nearly equivalent to those created with large amounts of gold-standard annotated data. |
Neural Machine Translation of Text from Non-Native Speakers (N19-1)
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| Challenge: | Neural Machine Translation (NMT) systems are known to degrade when confronted with noisy data. |
| Approach: | They propose to augment training data with sentences containing artificially-introduced grammatical errors to make the system more robust to such errors. |
| Outcome: | The proposed approach recovers 1.0 BLEU out of 2.4 BLUE lost due to grammatical errors on a set of Spanish translations of the JFLEG grammar error correction corpus. |
Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers (2024.naacl-short)
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| Challenge: | Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. |
| Approach: | They propose a method to extract distinguishing lexical features of dialects by utilizing interpretable dialect classifiers in the absence of human experts. |
| Outcome: | The proposed method extracts key language-specific lexical features that contribute to dialectal variations. |
Dataset Geography: Mapping Language Data to Language Users (2022.acl-long)
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| Challenge: | linguistic diversity and coverage of natural language processing systems is a key factor in determining quality of data available in the language field . lack of linguistic, typological, and geographical diversity is acknowledged and documented . but, the advent of massively multilingual models presents opportunity and hope for under-represented languages . |
| Approach: | They analyze the geographical representativeness of NLP datasets to determine their utility . they also explore economic and geographical factors that may explain the observed distributions . |
| Outcome: | The proposed model is representative of the language diversity and coverage of natural language processing systems. |
Tied Multitask Learning for Neural Speech Translation (N18-1)
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| Challenge: | Recent efforts in endangered language documentation focus on collecting spoken language resources . BULB project uses mobile app to collect spoken resources accompanied by spoken translations . |
| Approach: | They propose a model where the second task decoder receives information from the first task . they apply regularization that encourages transitivity and invertibility to the model . |
| Outcome: | The proposed model improves performance on low-resource speech transcription and translation tasks. |
Back to School: Translation Using Grammar Books (2024.emnlp-main)
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| Challenge: | Current large language models require massive amounts of parallel sentences to perform machine translations for high resource languages. |
| Approach: | They propose to incorporate grammar books into the prompt of GPT-4 to improve machine translation and evaluate the performance on 16 topologically diverse low-resource languages. |
| Outcome: | The proposed method improves on 16 low-resource languages on 16 topologically diverse languages. |
Should All Cross-Lingual Embeddings Speak English? (2020.acl-main)
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| Challenge: | lexicon induction evaluation dictionaries are mostly between English and another language, and the English hub is selected by default as the hub . lexiconic embeddings are often learned with a two-step process, whether under bilingual or multilingual settings. |
| Approach: | They propose to use English as the hub language for lexicon induction evaluation . they also expand a standard English-centered evaluation dictionary collection to include all language pairs . |
| Outcome: | The proposed method can significantly improve lexicon induction performance over multiple languages. |
Language and Speech Technology for Central Kurdish Varieties (2024.lrec-main)
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| Challenge: | a recent study focused on the Kurdish language, a less-resourced Indo-European language spoken by over 30 million speakers. |
| Approach: | They propose to develop resources for language and speech technology for Kurdish . they report the performance of machine translation, automatic speech recognition and language identification . |
| Outcome: | The proposed model is based on transcribing movies and TV series as an alternative to fieldwork. |
Mitigating Societal Harms in Large Language Models (2023.emnlp-tutorial)
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| Challenge: | Recent studies have highlighted societal harms that can be caused by language generation models deployed in the wild. |
| Approach: | They propose to use a typology of technical approaches to mitigating harms of language generation models to provide an overview of potential social issues in language generation including toxicity, social biases, misinformation, factual inconsistency, and privacy violations. |
| Outcome: | The proposed typology addresses toxicity, biases, misinformation, factual inconsistency, and privacy violations in language generation models. |
BiasDora: Exploring Hidden Biased Associations in Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies on social biases focus on a limited set of documented associations, such as gender-profession or race-crime. |
| Approach: | They propose to examine hidden, implicit bias associations across 9 bias dimensions by probing VLMs to uncover hidden, unexamined associations. |
| Outcome: | The proposed methods reveal that biases vary in negativity, toxicity, and extremity. |
Global Gallery: The Fine Art of Painting Culture Portraits through Multilingual Instruction Tuning (2024.naacl-long)
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| Challenge: | This study examines the ability of Large Language Models to encapsulate cultural nuances across diverse linguistic landscapes. |
| Approach: | They examine the efficacy of language-specific instruction tuning and the impact of pretraining on dominant language data in Large Language Models. |
| Outcome: | The findings highlight a nuanced landscape, with inconsistencies and biases, particularly in non-Western cultures. |
OCR Post Correction for Endangered Language Texts (2020.emnlp-main)
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| Challenge: | Currently, there is little to no data available to build natural language processing models for endangered languages. |
| Approach: | They propose a benchmark dataset of transcriptions for scanned books in three critically endangered languages and a method to improve OCR in these data-scarce settings. |
| Outcome: | The proposed method reduces the recognition error rate by 34% across the three endangered languages. |
GlobalBench: A Benchmark for Global Progress in Natural Language Processing (2023.emnlp-main)
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Yueqi Song, Simran Khanuja, Pengfei Liu, Fahim Faisal, Alissa Ostapenko, Genta Winata, Alham Aji, Samuel Cahyawijaya, Yulia Tsvetkov, Antonios Anastasopoulos, Graham Neubig
| Challenge: | despite advances in NLP, significant disparities in performance across languages still exist . prior benchmarks focused on a limited number of tasks and languages, but now GlobalBench tracks progress on all languages. |
| Approach: | They propose to use global benchmarks to track progress on all NLP datasets in all languages. |
| Outcome: | a new tool tracks progress on all NLP datasets in all languages and tracks per-speaker utility and equity . globalbench is designed to identify the most under-served languages and reward research efforts . a globalbech is available at https://github.com/neulab/globalbench. |
Pushing the Limits of Low-Resource Morphological Inflection (D19-1)
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| Challenge: | Recent advances in morphological inflection generation have limited resources . antonisa and colleagues present a battery of improvements to improve performance under low-resource conditions . |
| Approach: | They propose a two-step attention architecture for the inflection decoder that uses two-segments attention and a multi-single-syllabic attention architecture. |
| Outcome: | The proposed model outperforms the state-of-the-art in low-resource languages by 15 percentage points . the proposed model also shows that it can be used to model monolingual data hallucinations . |
Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning (2023.findings-emnlp)
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| Challenge: | Language teachers need to be accessible and have the necessary resources to create effective content for their students. |
| Approach: | They propose to extract grammar descriptions from a natural text corpus that answer questions about morphosyntax and semantics from lexical corpus. |
| Outcome: | The proposed method is applied to two Indian languages, Kannada and Marathi, which, unlike English, do not have well-developed resources for second language learning. |
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (D19-1)
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| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
| Outcome: | The proposed model outperforms strong baselines on the GLUE benchmark and can be adapted to new tasks efficiently and effectively. |
BembaSpeech: A Speech Recognition Corpus for the Bemba Language (2022.lrec-1)
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| Challenge: | Existing speech recognition systems for African languages are very low . lack of resources (speech and text) can be attributed to poor quality of speech. |
| Approach: | They present a preprocessed, ready-to-use automatic speech recognition corpus, BembaSpeech, consisting of 24 hours of read speech in the Bemba language. |
| Outcome: | The proposed model achieves a word error rate (WER) of 32.91% on the Bemba language . the 1 billion XLS-R parameter model achieve better performance than the monolingual pre-trained English model on the corpus. |
Evaluating the Morphosyntactic Well-formedness of Generated Texts (2021.emnlp-main)
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Adithya Pratapa, Antonios Anastasopoulos, Shruti Rijhwani, Aditi Chaudhary, David R. Mortensen, Graham Neubig, Yulia Tsvetkov
| Challenge: | Text generation systems are ubiquitous in natural language processing applications, but evaluation of these systems remains a challenge, especially in multilingual settings. |
| Approach: | They propose a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
| Outcome: | The proposed metric can evaluate the morphosyntactic well-formedness of text using its dependency parse and morphologically-rich rules of the language. |
Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)
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| Challenge: | Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines. |
| Approach: | They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain. |
| Outcome: | The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
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| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
| Outcome: | The proposed approaches take advantage of existing resources to produce QA systems in multiple languages. |
The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? (2024.emnlp-main)
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| Challenge: | Large language models have shown capabilities close to human performance in various analytical tasks. |
| Approach: | They investigate the efficiency and accuracy of Large Language Models in specialized tasks . they integrate LLMs with expert annotators to observe the impact of LLM suggestions . |
| Outcome: | The proposed model improves task completion speed but introduces anchoring bias . the proposed model is not suitable for open-ended analysis, but is capable of handling specialized tasks. |
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings (D19-1)
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| Challenge: | Recent studies have focused on domain adaptation for neural machine translation systems where in-domain data is scarce or nonexistent. |
| Approach: | They propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. |
| Outcome: | The proposed model performs better in multiple experimental settings and with back translation. |
SD-QA: Spoken Dialectal Question Answering for the Real World (2021.findings-emnlp)
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| Challenge: | Existing QA benchmarks do not account for errors that speech recognition models might introduce . evaluating production-ready QA systems on data that is not representative of real-world inputs is problematic . |
| Approach: | They construct a multi-dialect, spoken QA benchmark on five languages with 68k audio prompts in 24 dialects from 255 speakers. |
| Outcome: | The proposed model is based on 68k audio prompts in 24 dialects from 255 speakers. |
Generalized Data Augmentation for Low-Resource Translation (P19-1)
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| Challenge: | Low-resource language pairs with a lack of parallel data pose challenges for machine translation . data augmentation using monolingual data is an effective way to alleviate the problem . |
| Approach: | They propose a general framework for data augmentation for low-resource machine translation using monolingual data and a related high-resourced language. |
| Outcome: | The proposed method improves translation quality by 1.5 to 8 BLEU points under extreme low-resource settings compared to baselines. |
Follow the Beaten Path: The Role of Route Patterns on Vision-Language Navigation Agents Generalization Abilities (2025.naacl-long)
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
| Approach: | They propose a solution that combines visual and linguistic features to enable VLN . they propose augmentation of the training data to fill the gap in missing patterns . |
| Outcome: | The proposed solution fills the gap in missing patterns of training data. |
CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation (2024.findings-eacl)
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| Challenge: | Neural machine translation systems exhibit limited robustness in handling source-side linguistic variations. |
| Approach: | They propose a dialectal benchmark to quantify the robustness of MT systems to handle source-side linguistic variations. |
| Outcome: | The proposed benchmark demonstrates that large MT models face challenges translating dialectal variants. |
A Study on Scaling Up Multilingual News Framing Analysis (2024.findings-naacl)
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| Challenge: | Existing studies on media framing have focused on English only data, leaving a gap in research concerning multilingual contexts. |
| Approach: | They propose to use crowd-sourced datasets to automate framing analysis by automating translation and annotation. |
| Outcome: | The proposed system improves on existing models in Bengali and Portuguese . the proposed system can train on a crowd-sourced dataset in 12 languages . |
UniMorph 4.0: Universal Morphology (2022.lrec-1)
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Khuyagbaatar Batsuren, Omer Goldman, Salam Khalifa, Nizar Habash, Witold Kieraś, Gábor Bella, Brian Leonard, Garrett Nicolai, Kyle Gorman, Yustinus Ghanggo Ate, Maria Ryskina, Sabrina Mielke, Elena Budianskaya, Charbel El-Khaissi, Tiago Pimentel, Michael Gasser, William Abbott Lane, Mohit Raj, Matt Coler, Jaime Rafael Montoya Samame, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Arturo Oncevay, Juan López Bautista, Gema Celeste Silva Villegas, Lucas Torroba Hennigen, Adam Ek, David Guriel, Peter Dirix, Jean-Philippe Bernardy, Andrey Scherbakov, Aziyana Bayyr-ool, Antonios Anastasopoulos, Roberto Zariquiey, Karina Sheifer, Sofya Ganieva, Hilaria Cruz, Ritván Karahóǧa, Stella Markantonatou, George Pavlidis, Matvey Plugaryov, Elena Klyachko, Ali Salehi, Candy Angulo, Jatayu Baxi, Andrew Krizhanovsky, Natalia Krizhanovskaya, Elizabeth Salesky, Clara Vania, Sardana Ivanova, Jennifer White, Rowan Hall Maudslay, Josef Valvoda, Ran Zmigrod, Paula Czarnowska, Irene Nikkarinen, Aelita Salchak, Brijesh Bhatt, Christopher Straughn, Zoey Liu, Jonathan North Washington, Yuval Pinter, Duygu Ataman, Marcin Wolinski, Totok Suhardijanto, Anna Yablonskaya, Niklas Stoehr, Hossep Dolatian, Zahroh Nuriah, Shyam Ratan, Francis M. Tyers, Edoardo M. Ponti, Grant Aiton, Aryaman Arora, Richard J. Hatcher, Ritesh Kumar, Jeremiah Young, Daria Rodionova, Anastasia Yemelina, Taras Andrushko, Igor Marchenko, Polina Mashkovtseva, Alexandra Serova, Emily Prud’hommeaux, Maria Nepomniashchaya, Fausto Giunchiglia, Eleanor Chodroff, Mans Hulden, Miikka Silfverberg, Arya D. McCarthy, David Yarowsky, Ryan Cotterell, Reut Tsarfaty, Ekaterina Vylomova
| Challenge: | The Universal Morphology project provides broad-coverage instantiated morphological inflection tables for hundreds of diverse languages. |
| Approach: | They propose a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. |
| Outcome: | The proposed schema has added 66 new languages, including 24 endangered languages. |
Noisy Parallel Data Alignment (2023.findings-eacl)
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| Challenge: | Optical character recognition (OCR) is used to convert endangered language documents into machine-readable data, but its noisy outputs are a challenge for many under-resourced languages. |
| Approach: | They propose to use optical character recognition (OCR) to convert endangered language documents into machine-readable data by using noisy alignment models. |
| Outcome: | The proposed model reduces alignment error rate on a state-of-the-art neural-based alignment model up to 59.6%. |
Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
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| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
| Outcome: | The proposed language models can reflect and often amplify the effects of bias across linguistic, cultural, and societal borders. |
Script Normalization for Unconventional Writing of Under-Resourced Languages in Bilingual Communities (2023.acl-long)
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| Challenge: | linguistically under-represented communities have an extraordinary opportunity to create content in their native languages. |
| Approach: | They propose to solve the problem of script normalization for languages written in a Perso-Arabic script and use a transformer-based model to analyze the noise levels. |
| Outcome: | The proposed model can normalize a language written in a Perso-Arabic script and improve machine translation and language identification tasks. |
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023.eacl-main)
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| Challenge: | Recent advances in the capacity of large language models to generate human-like text have prompted a heated discourse around the risks of societal harms they introduce. |
| Approach: | They propose a taxonomy of interventions organized around the different phases where they can be adopted to mitigate harms. |
| Outcome: | The proposed methods are based on several prior works’ taxonomies of language model risks and provide an overview of strategies for detecting and ameliorating different kinds of risks/harms. |
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models (2021.naacl-main)
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| Challenge: | Language models are a new standard to build state-of-the-art NLP systems. |
| Approach: | They compare multilingual and monolingual models on unseen languages . they show that some languages benefit from transfer learning whereas others don't . |
| Outcome: | The proposed model behaves in multiple ways on unseen languages, while others fail to transfer . the results provide a promising direction towards making multilingual models useful for a new set of unseense languages. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
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| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
Dictionary-Aided Translation for Handling Multi-Word Expressions in Low-Resource Languages (2024.findings-acl)
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| Challenge: | Multi-word expressions (MWEs) are a challenging task in natural language processing . they are defined as combinations of at least two words with distinct lexical, morphological, syntactic, semantic or statistical characteristics. |
| Approach: | They propose a method leveraging an available out-of-context lexicon to improve translations . they propose to use a dictionary-aided translation to better translate multi-word expressions based on human annotations. |
| Outcome: | The proposed method improves translations comparable to those of a human speaker. |
A Resource for Computational Experiments on Mapudungun (2020.lrec-1)
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Mingjun Duan, Carlos Fasola, Sai Krishna Rallabandi, Rodolfo Vega, Antonios Anastasopoulos, Lori Levin, Alan W Black
| Challenge: | Low-resource languages still lag behind in documenting endangered languages . a large corpus of culturally significant conversations is available for computational experiments . |
| Approach: | They propose a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile. |
| Outcome: | The proposed corpus provides 142 hours of culturally significant conversations in Mapudungun . the language is spoken by the Mapuche people of southern Chile and western argentina . |
Lexically Aware Semi-Supervised Learning for OCR Post-Correction (2021.tacl-1)
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| Challenge: | Existing methods for digitizing text in endangered languages rely on manual data curated by the user. |
| Approach: | They propose a semi-supervised learning method that utilizes raw images to improve performance. |
| Outcome: | The proposed method reduces errors by 15%–29% on four endangered languages. |
Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations (2020.coling-main)
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| Challenge: | Documentation is not a cure-all for language loss, but it is an important part of language preservation. |
| Approach: | They propose to use multi-source neural models to create automatic glossing models . they also explore cross-lingual transfer and a simple output length control mechanism . |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource scenarios. |
Dialectal Toxicity Detection: Evaluating LLM-as-a-Judge Consistency Across Language Varieties (2025.findings-emnlp)
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| Challenge: | toxicity detection of modern LLMs is underexplored due to dialectal differences. |
| Approach: | They evaluate toxicity detection by using LLMs as evaluators across diverse dialects . they create a multi-dialect dataset using synthetic transformations and human-assisted translations based on human-aided translations. |
| Outcome: | The proposed model shows that LLMs are sensitive to dialectal shifts and low-resource multilingual variation. |
Endangered Languages meet Modern NLP (2020.coling-tutorials)
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| Challenge: | This tutorial will focus on NLP for endangered languages documentation and revitalization. |
| Approach: | This tutorial will focus on NLP for endangered languages documentation and revitalization . the goal is to motivate more NLP practitioners to work towards this important direction . |
| Outcome: | This tutorial will acquaint attendees with the process and the challenges of language documentation and revitalization. |
mHumanEval - A Multilingual Benchmark to Evaluate Large Language Models for Code Generation (2025.naacl-long)
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| Challenge: | Current evaluations focus on English-to-Python conversion tasks with limited test cases . code generation from low-resource language prompts remains largely unexplored . |
| Approach: | They propose a benchmark that supports prompts in over 200 natural languages . they provide expert human translations for 15 diverse natural languages (NLs) |
| Outcome: | The HumanEval Benchmark is the most widely used code generation benchmark . it provides expert human translations for 15 diverse natural languages . |
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)
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Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
| Challenge: | Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages. |
| Approach: | They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem . |
| Outcome: | The proposed model predicts good transfer languages much better than baselines considering single features in isolation. |
AlloVera: A Multilingual Allophone Database (2020.lrec-1)
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David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W Black, Florian Metze, Graham Neubig
| Challenge: | Phonemes are contrastive phonological units, and allophones are their various concrete realizations. |
| Approach: | They propose a resource that maps allophones to phonemes for 14 languages . they propose phonological representations that are much closer to a universal transcription . |
| Outcome: | The proposed resource maps from 218 allophones to phonemes for 14 languages. |
Cross-Lingual Representation Alignment Through Contrastive Image-Caption Tuning (2025.acl-short)
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| Challenge: | Multilingual alignment of sentence representations has mostly required bitexts to bridge the gap between languages. |
| Approach: | They propose to use image captions to implicitly align text representations between languages to make them usable for cross-lingual Natural Language Understanding (NLU) and bitext retrieval. |
| Outcome: | The proposed approach is usable for cross-lingual Natural Language Understanding (NLU) and bitext retrieval. |
Revisiting the Effects of Leakage on Dependency Parsing (2022.findings-acl)
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| Challenge: | Recent work shows that treebank size and linguistic variation are important factors that explain the variation in dependency parsing performance. |
| Approach: | They propose a measure of leakage that explains and correlates with observed performance variation. |
| Outcome: | The proposed measure explains and correlates with observed performance variation. |
Multilingual Native Language Identification with Large Language Models (2025.naacl-srw)
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| Challenge: | Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of individuals based on their second language production. |
| Approach: | They evaluated the performance of several LLMs on non-English NLI corpora compared to traditional statistical machine learning models and language-specific BERT-based models. |
| Outcome: | The proposed models outperform statistical models and language-specific BERT-based models on English, Italian, Norwegian, and Portuguese. |
It’s Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information (2020.acl-main)
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| Challenge: | Current state-of-the-art MT systems are based on neural networks, but it is unclear whether all translation directions are equally easy (or hard) to model for NMT. |
| Approach: | They propose an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. |
| Outcome: | The proposed metric allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty independent of the translation task. |