Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model (2023.acl-long)
Copied to clipboard
| Challenge: | We use both Bayesian and neural models to dissect a data set of Chinese learners’ pre- and post-interventional responses to two tests measuring their understanding of English prepositions. |
| Approach: | They use Bayesian and neural models to dissect Chinese learners' responses to two tests measuring their understanding of English prepositions. |
| Outcome: | The proposed model can predict grammaticality and learnability based on language model probabilities. |
Similar Papers
Predicting Reference: What do Language Models Learn about Discourse Models? (2020.emnlp-main)
Copied to clipboard
| Challenge: | a growing literature that probes neural language models to assess their latent acquisition of grammatical knowledge has not investigated their acquisition of discourse modeling ability. |
| Approach: | They draw on a psycholinguistic literature that has established how different contexts affect referential biases concerning who is likely to be referred to next. |
| Outcome: | The proposed models do not resemble human language users, the authors show . their models capture the linguistic knowledge required to perform discourse modeling . |
Deep Bayesian Learning and Understanding (C18-3)
Copied to clipboard
| Challenge: | COLING 2018 is a conference for researchers and practitioners working on machine learning and deep learning. |
| Approach: | a tutorial on machine learning and deep learning will be presented at COLING 2018 . the tutorial will focus on statistical models, deep neural networks, sequential learning and natural language understanding . |
| Outcome: | This tutorial will present the latest advances in deep Bayesian and sequential learning at COLING 2018 . |
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases (2025.acl-long)
Copied to clipboard
Rena Wei Gao, Xuetong Wu, Tatsuki Kuribayashi, Mingrui Ye, Siya Qi, Carsten Roever, Yuanxing Liu, Zheng Yuan, Jey Han Lau
| Challenge: | Large Language Models (LLMs) can simulate non-native-like English use observed in human second language (L2) learners interfered with by their native first language (N1) knowledge. |
| Approach: | They use large language models to simulate non-native-like English use observed in human second language (L2) learners, and then compare their outputs to real L2 learner data. |
| Outcome: | The proposed models replicate L1-dependent patterns observed in human second language (L2) learners, with distinct influences from various languages. |
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences (2022.coling-1)
Copied to clipboard
| Challenge: | Presuppositions are assumptions that are taken for granted by an utterance. |
| Approach: | They propose to use heuristics to create alternative "contrastive" test cases . they also analyze samples from ImpPres datasets to better understand their predictions . |
| Outcome: | The proposed model performs better on the ImpPres dataset than on the other datasets. |
Second Language Acquisition of Neural Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | a recent study examined the cross-lingual transferability of neural language models . previous studies focused on their first language acquisition . |
| Approach: | They propose to pretrain bilingual LMs with a scenario similar to human L2 acquisition . they find that pretraining accelerated their linguistic generalization in L2 . |
| Outcome: | The results show that pretraining bilingual LMs accelerates their linguistic generalizations . the results clarify their (non-)human-like L2 acquisition in particular aspects . |
Chinese Grammatical Correction Using BERT-based Pre-trained Model (2020.aacl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that pre-trained models improve performance on downstream tasks. |
| Approach: | They propose to incorporate a pre-trained model into an encoder-decoder model to improve the performance of Chinese grammatical error correction tasks. |
| Outcome: | The proposed method improves the performance of Chinese grammatical error correction tasks. |
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (P19-1)
Copied to clipboard
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
| Challenge: | State-of-the-art models in natural language processing (NLP) often incorporate sentence encoder functions which generate a sequence of vectors intended to represent the in-context meaning of each word in an input text. |
| Approach: | They conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks as alternatives and complements to language modeling. |
| Outcome: | The proposed model can be used to train sentences on language modeling tasks. |
Deep Bayesian Natural Language Processing (P19-4)
Copied to clipboard
| Challenge: | Introduction to deep Bayesian learning for natural language addresses the fundamentals of statistical models and neural networks. |
| Approach: | This tutorial addresses the advances in deep Bayesian learning for natural language . it focuses on advanced Bayessian models and deep models . authors present case studies and domain applications to tackle different issues . |
| Outcome: | This tutorial focuses on advanced Bayesian models and deep models for natural language . case studies and domain applications are presented to tackle different issues in deep Bayessian processing, learning and understanding. |
Modeling Nonnative Sentence Processing with L2 Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that while all of the LMs’ word surprisals improve prediction of L2 reading times, there is no reliable effect of the choice of L1’s L1. |
| Approach: | They pretrain GPT2 on 6 different first languages, followed by English as the second language (L2). |
| Outcome: | The pretraining of L1 improves prediction of L2 reading times, but there is no reliable effect of the pretraining L1 on the model's performance on English speakers. |
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition (2020.acl-main)
Copied to clipboard
| Challenge: | Natural language inference (NLI) is an increasingly important task for natural language understanding . however, the ability of NLI models to make pragmatic inferences remains understudied . |
| Approach: | They use semi-automatically generated sentence pairs to evaluate whether NLI models make pragmatic inferences. |
| Outcome: | The proposed model trains on multiNLI and shows that it learns to draw pragmatic inferences. |