Abhirut Gupta, Ananya B. Sai, Richard Sproat, Yuri Vasilevski, James Ren, Ambarish Jash, Sukhdeep Sodhi, Aravindan Raghuveer
| Challenge: | Increasingly, people are forced to use the Web in languages they have low literacy in due to technology asymmetries. |
| Approach: | They propose a method to mine phoneme confusions for pairs of L1 and L2 and plug them into a generative model for synthetically producing corrupted L2 text. |
| Outcome: | The proposed method corrupts the popular language understanding benchmark SuperGLUE and improves performance. |
Similar Papers
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling (2022.lrec-1)
Copied to clipboard
| Challenge: | Current state-of-the-art acoustic models can easily comprise more than 100 million parameters. |
| Approach: | They propose to train a gender-balanced, multilingual corpus from 76.000 contributors via Mozilla’s Common Voice project to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. |
| Outcome: | The proposed model can perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
Credible without Credit: Domain Experts Assess Generative Language Models (2023.acl-short)
Copied to clipboard
| Challenge: | ChatGPT has been criticized for its lack of accuracy and coherence . authors argue that language models could replace search engines and make college essays obsolete . |
| Approach: | a team of 10 domain experts conducts an initial assessment of language models using 100 expert-written questions. |
| Outcome: | The results show that language models are mixed in their accuracy. |
Toward Joint Language Modeling for Speech Units and Text (2023.findings-emnlp)
Copied to clipboard
Ju-Chieh Chou, Chung-Ming Chien, Wei-Ning Hsu, Karen Livescu, Arun Babu, Alexis Conneau, Alexei Baevski, Michael Auli
| Challenge: | Speech and text are two major forms of human language and little effort has been made to model them together. |
| Approach: | They propose to combine speech and text models to create mixed speech-text data by using different tokenizers and automatic metrics to evaluate how well the model mixes speech and texts. |
| Outcome: | The proposed model improves over a speech-only baseline and shows zero-shot cross-modal transferability. |
Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization (2024.emnlp-main)
Copied to clipboard
| Challenge: | Xue et al., 2021) show that large language models suffer from performance degradation on unseen closely-related languages and dialects relative to their high-resource language neighbour (HRLN). |
| Approach: | They propose to model phonological, morphological, and lexical distance as Bayesian noise processes to synthesize artificial languages that are controllably distant from the HRLN. |
| Outcome: | The proposed model offers insights on model robustness to isolated and composed linguistic phenomena and the impact of task and HRL characteristics on PD. |
What Kind of Language Is Hard to Language-Model? (P19-1)
Copied to clipboard
| Challenge: | a recent study suggests that language models perform poorly across languages. |
| Approach: | They propose a model that fits a paired-sample multiplicative mixed-effects model to obtain language difficulty coefficients from at least-pairwise parallel corpora. |
| Outcome: | The proposed model is able to handle missing data and is aware of inter-sentence variation. |
Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent phoneme classifiers treat allophonic variation as a single phoneme . atypical pronunciation assessment requires distinguishing between a typical and asymmetric pronunciations . |
| Approach: | They propose a new approach that leverages Gaussian mixture models to model phoneme distributions with multiple subclusters. |
| Outcome: | The proposed approach achieves state-of-the-art across dysarthric and non-native speech datasets. |
JGLUE: Japanese General Language Understanding Evaluation (2022.lrec-1)
Copied to clipboard
| Challenge: | There is no benchmark for Japanese to evaluate and analyze NLU ability from different perspectives. |
| Approach: | They build a Japanese NLU benchmark from scratch without translation to measure general NLU ability in Japanese. |
| Outcome: | a Japanese NLU benchmark is built from scratch without translation to measure general NLU ability in Japanese. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |
Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)
Copied to clipboard
| Challenge: | Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models . |
| Approach: | They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths. |
| Outcome: | The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%. |