Papers with language learning
LingConv: An Interactive Toolkit for Controlled Paraphrase Generation with Linguistic Attribute Control (2025.emnlp-demos)
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| Challenge: | LINGCONV is an interactive toolkit for controllable text generation . it allows fine-grained control over 40 specific linguistic attributes spanning lexical, syntactic, and discourse dimensions. |
| Approach: | They propose a toolkit for paraphrase generation that allows finegrained control over 40 specific linguistic attributes. |
| Outcome: | The toolkit is available at https://mohdelgaar-lingconv.hf.space, with a demo video at https:youtu.be/wRBJEJ6EALQ. |
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers (2022.starsem-1)
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| Challenge: | lexical collocations exhibit varying degrees of frozenness due to their varying degree of frozenncy. |
| Approach: | They propose a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture and evaluate the task of collocation recognition in context. |
| Outcome: | The proposed model encoding syntactic dependencies is useful, and provides insights on differences in collocation typification in English, Spanish and French. |
Automatic Gloss Dictionary for Sign Language Learners (2022.acl-demo)
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| Challenge: | 430 million people worldwide have developed hearing loss and 700 million more are learning a sign language as a second language . sign language learners have limited means of seeking assistance and are restricted to class offerings or relying on a webcam to look up the sign. |
| Approach: | They propose an online tool supporting 2, 000 signs to assist language learners in determining the meaning of given signs. |
| Outcome: | The proposed system can lower the barrier in sign language learning by addressing the common problem of sign finding and make it accessible to the wider community. |
Template-guided Grammatical Error Feedback Comment Generation (2023.eacl-srw)
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| Challenge: | Writing corrective feedback on learner text is widespread in language education, but it can be time-consuming for teachers. |
| Approach: | They propose to use feedback comment generation to generate explanatory notes for learners by categorizing comments and constraining outputs of noisy classes. |
| Outcome: | The proposed scheme can be used to generate feedback comment corpora using a broader scope than existing typologies focused on error correction. |
Automatically Suggesting Diverse Example Sentences for L2 Japanese Learners Using Pre-Trained Language Models (2024.acl-srw)
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| Challenge: | Pre-trained language models (PLMs) are used to produce examples sentences targeting L2 learners. |
| Approach: | They propose to use pre-trained language models to produce diverse examples of Japanese sentences that are aligned with learners’ proficiency levels. |
| Outcome: | The proposed method is adaptable to other languages with minor adjustments. |
Towards Multi-Modal Text-Image Retrieval to improve Human Reading (2021.naacl-srw)
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| Challenge: | In primary school, children's books, as well as in modern language learning apps, multi-modal learning strategies like illustrations of terms and phrases are used to support reading comprehension. |
| Approach: | They propose to use multi-modal transformers to train multi-dimensional models on text-image retrieval to support a user's reading comprehension of arbitrary text. |
| Outcome: | The proposed model performs poorly because of the short and relatively simple textual data that the current models are trained with. |
Pretraining on Interactions for Learning Grounded Affordance Representations (2022.starsem-1)
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| Challenge: | Existing studies of affordances have not integrated into formal semantics. |
| Approach: | They propose to integrate 3D objects' trajectories into a neural network to predict their traversories. |
| Outcome: | The proposed model outperforms 2D computer vision models and is more accurate than expected. |
Exploring the Semantic Space of Second Language Learners (2026.eacl-srw)
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| Challenge: | Using machine learning models, we compared the semantic space of university-level students learning French with native speakers' (L1) . |
| Approach: | They extracted semantic features from narrative text and used interpretability techniques to identify the most informative features per model. |
| Outcome: | The results show that the second language learners had higher semantic similarity scores than the native speakers at the token level, whereas the similarity decreased over time but did not reach native-level values. |
Controlled Language Generation for Language Learning Items (2022.emnlp-industry)
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| Challenge: | Recent advances in pre-trained language models have resulted in success in generating fluent English text. |
| Approach: | They propose to employ natural language generation to rapidly generate English language items . they experiment with deep pretrained models and develop methods for controlling items for factors relevant in language learning . |
| Outcome: | The proposed framework shows high grammatically scores for all models and higher complexity over baseline models. |
Towards Hierarchical Spoken Language Disfluency Modeling (2024.eacl-long)
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| Challenge: | Existing solutions to speech dysfluency modeling are limited and expensive for low-income families. |
| Approach: | They propose a hierarchical unconstrained dysfluency modeling approach that addresses both dysfluencies transcription and detection to eliminate the need for extensive manual annotation. |
| Outcome: | The proposed approach eliminates the need for extensive manual annotation and improves the accuracy of the proposed model in phonetic transcription. |
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning (2020.lrec-1)
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Lionel Nicolas, Verena Lyding, Claudia Borg, Corina Forascu, Karën Fort, Katerina Zdravkova, Iztok Kosem, Jaka Čibej, Špela Arhar Holdt, Alice Millour, Alexander König, Christos Rodosthenous, Federico Sangati, Umair ul Hassan, Anisia Katinskaia, Anabela Barreiro, Lavinia Aparaschivei, Yaakov HaCohen-Kerner
| Challenge: | Lack of wide-coverage and high-quality LRs is a longstanding issue in natural language processing (NLP) however, there are no large initiatives of similar scale for creating new LR or improving existing ones. |
| Approach: | They propose a generic approach to combine implicit crowdsourcing and language learning to mass-produce language resources (LRs) they describe its core paradigm that consists in pairing specific types of LRs with specific exercises . |
| Outcome: | The proposed approach can be used in several learning scenarios to produce a multitude of NLP resources and alleviate the long-standing issue of the lack of LRs. |
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems (2024.emnlp-main)
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| Challenge: | Existing large language models (LLMs) can be adopted as tutoring agents for math and language learning. |
| Approach: | They propose a framework to construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario. |
| Outcome: | The proposed framework can construct profiles of different student groups by refining and integrating both cognitive and noncognitive aspects, and leverage LLMs for personality-aware student simulation in a language learning scenario. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
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| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |
Perceiving the World: Question-guided Reinforcement Learning for Text-based Games (2022.acl-long)
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| Challenge: | Text-based games provide an interactive way to study natural language processing. |
| Approach: | They propose a two-phase training framework to decouple language learning from reinforcement learning and improve the sample efficiency. |
| Outcome: | The proposed method significantly improves performance and sample efficiency against compound error and limited pre-training data. |
SchAman: Spell-Checking Resources and Benchmark for Endangered Languages from Amazonia (2022.aacl-short)
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Arturo Oncevay, Gerardo Cardoso, Carlo Alva, César Lara Ávila, Jovita Vásquez Balarezo, Saúl Escobar Rodríguez, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Juan López Bautista, Nimia Acho Rios, Remigio Zapata Cesareo, Héctor Erasmo Gómez Montoya, Roberto Zariquiey
| Challenge: | Spell-checking as a generation task requires large amount of data, which is not feasible for endangered languages such as the languages spoken in Peru. |
| Approach: | They propose to use augmented misspelling data to train neural spell-checking models for four endangered languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine . |
| Outcome: | The proposed model achieves better scores in most of the errors and languages in the four indigenous languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine. |
Communication Drives the Emergence of Language Universals in Neural Agents: Evidence from the Word-order/Case-marking Trade-off (2023.tacl-1)
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| Challenge: | Existing models of language learning with neural agents lack appropriate cognitive biases in artificial learners. |
| Approach: | They propose a framework where speaking and listening agents learn a miniature language via supervised learning and optimize it for communication via reinforcement learning. |
| Outcome: | The proposed framework replicates the word-order/case-marking trade-off without hard-coding biases in the agents. |
Large-Scale Bitext Corpora Provide New Evidence for Cognitive Representations of Spatial Terms (2024.eacl-long)
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| Challenge: | Recent evidence suggests that there exist two classes of cognitive representations within the spatial terms of a language. |
| Approach: | They propose a pipeline for extracting, isolating, and aligning spatial terms from parallel text . they find evidence that variability in functional terms differs significantly from that of geometric terms . |
| Outcome: | The proposed pipeline extracts, isolates, and aligns spatial terms in basic locative constructions from parallel text. |
MuLVE, A Multi-Language Vocabulary Evaluation Data Set (2022.lrec-1)
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| Challenge: | Existing systems for vocabulary evaluation are based on simple rules and do not account for real-life user learning data. |
| Approach: | They propose to use real-life user vocabulary learning data to evaluate vocabulary . they use language learning data from a phase6 vocabulary trainer to generate a multilingual data set for vocabulary evaluation. |
| Outcome: | The proposed data set provides outstanding results with 95.5 accuracy and F2-score. |
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes (2024.naacl-long)
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| Challenge: | Modern neural language models (LMs) require distinctly un-human-like ways to achieve these results. |
| Approach: | They train a diverse set of LM architectures with and without auxiliary visual supervision on datasets of varying scales. |
| Outcome: | The proposed models exhibit better learning of syntactic categories, lexical relations, semantic features, word similarity and alignment with human neural representations. |
SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning (2025.acl-industry)
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Zhengyuan Liu, Geyu Lin, Hui Li Tan, Huayun Zhang, Yanfeng Lu, Xiaoxue Gao, Stella Xin Yin, Sun He, Hock Huan Goh, Lung Hsiang Wong, Nancy F. Chen
| Challenge: | Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels. |
| Approach: | They propose a dialogic tutor designed to facilitate language learning through picture description tasks. |
| Outcome: | Empirical studies show that SingaKids provides effective dialogic teaching, benefiting learners at different performance levels. |
Controlling Reading Ease with Gaze-Guided Text Generation (2026.eacl-long)
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| Challenge: | Using a gaze-based model, we generate texts with controllable reading ease. |
| Approach: | They propose a method that predicts gaze patterns to steer language model outputs towards eliciting certain reading behaviors by predicting eye-tracking measures. |
| Outcome: | The proposed method generates texts with controllable reading ease using eye-tracking with native and non-native speakers of English. |
Arabic Diacritization Using Morphologically Informed Character-Level Model (2024.lrec-main)
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Muhammad Morsy Elmallah, Mahmoud Reda, Kareem Darwish, Abdelrahman El-Sheikh, Ashraf Hatim Elneima, Murtadha Aljubran, Nouf Alsaeed, Reem Mohammed, Mohamed Al-Badrashiny
| Challenge: | Diacritics are typically omitted in Arabic writings and the reader needs to guess the proper diacritics as they are reading. |
| Approach: | They propose a morphologically informed character-level model that can recover both types of diacritics simultaneously. |
| Outcome: | The proposed model achieves lowest word-level diacritization error rate for Classical Arabic, MSA, and two dialectal Arabic texts. |
Read to Hear: A Zero-Shot Pronunciation Assessment Using Textual Descriptions and LLMs (2025.emnlp-main)
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| Challenge: | Automatic pronunciation assessment is typically performed by acoustic models trained on audio-score pairs. |
| Approach: | They propose a zero-shot, textual description-based Pronunciation Assessment approach that utilizes human-readable representations of speech signals fed into an LLM to assess pronunciation accuracy and fluency. |
| Outcome: | The proposed approach is cost-efficient and competitive in performance . it significantly improves the performance of conventional audio-score-trained models on out-of-domain data . |
Opportunities and Challenges in Neural Dialog Tutoring (2023.eacl-main)
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| Challenge: | Existing approaches to designing dialog tutors have been challenging . current approaches perform poorly in constrained learning scenarios, authors find . |
| Approach: | They analyze dialog tutoring models using automatic and human evaluations to understand the new opportunities brought by dialog tutors. |
| Outcome: | The proposed models perform poorly in less constrained learning scenarios, the authors show . they find large number of model reasoning errors in 45% of conversations . |
EDEN: Empathetic Dialogues for English Learning (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that student passion and perseverance, or grit, is associated with language learning success. |
| Approach: | They hypothesize that as students perceive their English teachers to be more supportive, their grit improves. |
| Outcome: | The proposed chatbot improves student persistence in learning a second language. |
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game (P18-1)
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| Challenge: | supervised language learning is limited by the ability of capturing mainly the statistics of training data. |
| Approach: | They propose to use conversational games to train agents to use new knowledge . they propose to mimic and reinforce conversational game and use it in one-shot fashion . |
| Outcome: | The proposed approach is able to acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion. |
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. |
ClozEx: A Task toward Generation of English Cloze Explanation (2023.findings-emnlp)
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| Challenge: | Existing tasks and datasets specifically designed for generating language learner explanations for cloze questions are lacking . clozing questions are used to assess language proficiency and enhance language learning . |
| Approach: | They propose a task ClozEx to generate explanations for cloze questions in LA . they use a curated dataset of clozing questions paired with explanations . |
| Outcome: | The proposed task generates fluent explanations for cloze questions in English as a second language learners. |
Error-preserving Automatic Speech Recognition of Young English Learners’ Language (2024.acl-long)
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| Challenge: | State-of-the-art speech recognition models are often trained on adult read-aloud data by native speakers and do not transfer well to young language learners’ speech. |
| Approach: | They propose to use an automated speech recognition module to train language learners' speaking skills on spontaneous speech by young language learners. |
| Outcome: | The proposed model improves on 85 hours of English audio spoken by Swiss learners and preserves their mistakes. |
The Abkhaz National Corpus (L18-1)
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| Challenge: | Abkhaz National Corpus is a comprehensive and open, grammatically annotated text corpus . it is currently growing and is being extended to include all important texts written in the language . |
| Approach: | They propose to use the Abkhaz National Corpus to annotate Abkhhaz texts . the corpus is a comprehensive and open, grammatically annotated text corpus . |
| Outcome: | The proposed corpus is a grammatically annotated text corpus which makes the language accessible to scientific investigations from various perspectives. |
Generation of a Spanish Artificial Collocation Error Corpus (L18-1)
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| Challenge: | collocations are combinations of two elements where one (the base) is freely chosen, despite the limitations of the other (collocate) current tools for collocation error detection and correction focus on collocation validation and identification of miscollocations . |
| Approach: | They propose an algorithm for automatic generation of an artificial collocation error corpus of american English learners of Spanish that includes 17 different types of collocation errors. |
| Outcome: | The proposed algorithm can detect and classify collocation errors in learners' writings . collocation error detection and correction has not received the attention it deserves . |
Interactive Word Completion for Morphologically Complex Languages (2020.coling-main)
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| Challenge: | morphologically complex languages have multiple morph slots with large or unbounded sets of fillers. |
| Approach: | They propose a method for morphologically-aware text input in Kunwinjku . they modify an existing finite state recognizer to map input morph prefixes to morph completions . |
| Outcome: | The proposed method is portable to Turkish and shows that it can be used in other languages. |
Pragmatically Informative Text Generation (N19-1)
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| Challenge: | Existing approaches to pragmatics have been used to improve the informativeness of generated text in grounded language learning problems. |
| Approach: | They propose to use pragmatics to improve the informativeness of conditional text models . they propose to apply pragmatic reasoning to more traditional language generation tasks . |
| Outcome: | The proposed methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations. |
Leveraging pre-trained language models for linguistic analysis: A case of argument structure constructions (2024.emnlp-main)
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| Challenge: | Argument structure constructions (ASCs) are lexicogrammatical patterns at the clausal level. |
| Approach: | They evaluate the effectiveness of pre-trained language models in identifying argument structure constructions . they use supervised training with RoBERTa and prompt-guided annotation with GPT-4 . |
| Outcome: | The proposed model outperforms the gold-standard model on three methods . the results show that the model performs better on gold-standardized data . |
CEFR-Based Sentence Difficulty Annotation and Assessment (2022.emnlp-main)
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| Challenge: | Controllable text simplification is a crucial assistive technique for language learning and teaching. |
| Approach: | They propose a sentence-level assessment model to handle unbalanced level distribution . previous studies have suggested that controllable text simplification is difficult to apply . |
| Outcome: | The proposed method outperforms baselines in readability assessment by scoring macro-F1 on the level assessment. |
Multi-Agent Language Learning: Symbolic Mapping (2023.findings-acl)
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| Challenge: | Recent work has focused on the emergence of language in cooperative tasks where neural network agents learn a communication protocol from scratch to solve problems together. |
| Approach: | They propose a task transfer method and symbolic mapping architecture to help agents learn a compositional and symmetric language in dialog games. |
| Outcome: | The proposed method can help agents learn a compositional and symmetric language in complex settings like dialog games and the proposed architecture promotes vocabulary expansion. |
Interpretability for Language Learners Using Example-Based Grammatical Error Correction (2022.acl-long)
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| Challenge: | Existing neural-based GEC models mainly aim at improving accuracy, but their interpretability has not been explored. |
| Approach: | They propose an example-based method that generates corrections using retrieved examples. |
| Outcome: | The proposed method improves interpretability and supports language learners. |
CEFR-based Lexical Simplification Dataset (L18-1)
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| Challenge: | Existing tools for lexical simplification are not tailored to language education with word levels and lists of candidates subjective. |
| Approach: | They construct a language dataset for lexical simplification based on CEFR levels . target and candidate words are assigned CEFR-J wordlists and English Vocabulary Profile . |
| Outcome: | The proposed method is based on the common European Framework of References for Languages (CEFR) levels and candidates are selected using an online thesaurus. |
Multilingual Dictionary Based Construction of Core Vocabulary (2020.lrec-1)
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| Challenge: | Existing methods for core vocabulary lists for multiple applications are lacking coverage in sparse dictionaries . we propose a new method for definition and construction of core vocabulary sets based on coverage in dictionary dictionaria . |
| Approach: | They propose a functional definition and construction method for core vocabulary sets based on relative coverage of a target concept in bilingual dictionaries. |
| Outcome: | The proposed method achieves high overlap with existing vocabulary lists . it uses a cognate prediction method to recover missing coverage of the vocabulary . |
Injecting structural hints: Using language models to study inductive biases in language learning (2023.findings-emnlp)
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| Challenge: | a recent study examines the cognitive inductive biases that make language learning possible. |
| Approach: | They structurally bias transformer language models by pretraining on synthetic data . they then evaluate their inductive biases by fine-tuning on three different languages . |
| Outcome: | The proposed method predisposes transformer models to three types of inductive biases . it also fine-tunes the models on three typologically-distant human languages . |
Continual Lifelong Learning in Natural Language Processing: A Survey (2020.coling-main)
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| Challenge: | Existing approaches to continual learning (CL) are costly and time-consuming. |
| Approach: | They propose to examine the problem of continual learning in NLP through the lens of various NLP tasks and provide a critical review of existing methods. |
| Outcome: | The proposed methods are critical to the development of CL models and provide a critical review of existing methods and datasets. |
Every Verb in Its Right Place? A Roadmap for Operationalizing Developmental Stages in the Acquisition of L2 German (2024.lrec-main)
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| Challenge: | Developmental stages are a linguistic concept claiming that language learning progresses in an ordered, step-like manner. |
| Approach: | They propose to translate a linguistic specification into a computational procedure that can assign clauses to a developmental stage based on verb placement. |
| Outcome: | The proposed system lacks a coherent linguistic specification of developmental stages . it also lacks the ability to translate the specification into a computational procedure based on verb placement. |
HADREB: Human Appraisals and (English) Descriptions of Robot Emotional Behaviors (2022.lrec-1)
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| Challenge: | HADREB datasets explore how humans perceive robot emotional states . emotions are a fundamental part of the human language system and are used as scaffolding for language learning . |
| Approach: | They present a dataset of human appraisals and English descriptions of robot emotional behaviors . they use mistyrobotics mist and digital dream labs cozmo robots to analyze the data . |
| Outcome: | The proposed dataset examines how humans perceive robot emotional states and how they relate to human language. |
Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors (2025.findings-acl)
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| Challenge: | Existing studies have focused on coding tutoring, but their capabilities in guiding users to solve complex tasks remain underexplored. |
| Approach: | They propose a novel agent workflow, Trace-and-Verify, which combines knowledge tracing to estimate a student’s knowledge state and turn-by-turn verification to ensure effective guidance toward task completion. |
| Outcome: | The proposed agent workflow achieves significantly higher success rates than existing tutoring agents. |
An SLA Corpus Annotated with Pedagogically Relevant Grammatical Structures (L18-1)
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| Challenge: | a study using a framework to evaluate a language learner's proficiency in a second language aims to examine the production of learners with pedagogically relevant grammatical structures . |
| Approach: | They annotated texts produced by language learners with grammatical structures . they found that learners from different proficiency levels use pedagogically relevant structures compared to those of already certified language learners . |
| Outcome: | The annotated resource SGATe analyzes texts produced by language learners with grammatical structures . structure evolution along levels and level in which they are used the most was studied . |
VROAV: Using Iconicity to Visually Represent Abstract Verbs (2020.lrec-1)
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| Challenge: | Visual languages like sign languages reveal enlightening patterns across signs of similar meanings, pointing towards the possibility of identifying clusters of iconic meanings. |
| Approach: | a new verb classification system is proposed to visually represent 20 classes of abstract verbs. |
| Outcome: | The proposed system could be used as a language learning aid or as linguistic comprehension tool for digital text. |
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)
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Zhendong Chu, Shen Wang, Jian Xie, Tinghui Zhu, Yibo Yan, Jingheng Ye, Aoxiao Zhong, Xuming Hu, Jing Liang, Philip S. Yu, Qingsong Wen
| Challenge: | Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes. |
| Approach: | This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems . |
| Outcome: | The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings. |
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. |
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (2023.emnlp-main)
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| Challenge: | Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. |
| Approach: | They argue that LLMs only parrot statistical patterns in training data and that language learning in LLM cannot inform human language learning. |
| Outcome: | The proposed model can generate grammatically correct, fluent text without requiring human intervention. |
The Effect of Efficient Messaging and Input Variability on Neural-Agent Iterated Language Learning (2021.emnlp-main)
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| Challenge: | Existing studies have focused on agent-based simulations of language emergence. |
| Approach: | They propose to model the trade-off between word order and inflection in natural languages by using neural network agents. |
| Outcome: | The results show that neural agents strive to maintain the utterance type distribution observed during learning, rather than developing a more efficient or systematic language. |
Learning from Children: Improving Image-Caption Pretraining via Curriculum (2023.findings-acl)
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| Challenge: | Image-caption pretraining is a difficult problem as it requires multiple concepts (nouns) from captions to be aligned to multiple objects in images. |
| Approach: | They propose a curriculum learning framework that uses images to align multiple concepts to multiple objects in an image. |
| Outcome: | The proposed learning framework improves over pretraining from scratch, using a pretrained image or/and text encoder, low data regime etc. |
Position: LLMs Can be Good Tutors in English Education (2025.emnlp-main)
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
| Approach: | They argue that large language models can be effective tutors in English education . they encourage interdisciplinary research to explore these roles, fostering innovation and risks . |
| Outcome: | The proposed models can play three critical roles: 1) as data enhancers, 2) as task predictors, 3) as agents, enabling personalized and inclusive education. |
Is a cute puyfred cute? Context-dependent form-meaning systematicity in LLMs (2025.findings-acl)
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| Challenge: | valence is encoded in meaningful ways in large language models and in some LLMs, pseudowords affect the representation of whole sentences similarly to words. |
| Approach: | They investigate how LLMs represent valence, a key semantic attribute, and how they deal with contextualisation of pseudowords in sentences. |
| Outcome: | The results show that the models represent valence, a key semantic attribute, in sentences and in context, and that they handle the contextualisation of pseudowords differently. |
Communicating with Speakers and Listeners of Different Pragmatic Levels (2024.emnlp-main)
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| Challenge: | Using a model of the speaker's intentions, people can achieve pragmatic interpretations using a variety of reasoning abilities. |
| Approach: | They propose to model the interaction between speakers and listeners with different levels of pragmatic competence and to model their level of reasoning abilities. |
| Outcome: | The proposed model is based on a simulating language learning and conversing between speakers and listeners with different levels of reasoning abilities. |