Papers with ADS
Learning to Understand Child-directed and Adult-directed Speech (2020.acl-main)
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| Challenge: | linguistic properties of child-directed speech differ from adult-directed in many ways . linguistic differences between CDS and ADS are retained, but the acoustic properties are similar. |
| Approach: | They compare the task performance of models trained on adult-directed speech and child-directed language . they propose that CDS is optimized for learnability, but not for comprehension . |
| Outcome: | The proposed model trains on adult-directed speech and child-directed language . the model generalizes better on the training register and on synthesized speech . |
SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models (2025.emnlp-main)
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| Challenge: | Existing large language models only support hundreds of languages, and they are usually limited in English. |
| Approach: | They propose a task to automatically select which dictionary to use to enhance translation . they call it Select Low-frequency Words!, which inherits advantage of dictionary-based methods . |
| Outcome: | The proposed method can save tokens and improve translation performance on 100 languages. |
Towards Building a Spoken Dialogue System for Argument Exploration (2022.lrec-1)
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| Challenge: | Argumentative dialogue systems lack a robust natural language understanding framework for complex tasks . drop-down menus hinder the application of natural language learning approaches . |
| Approach: | They propose to integrate a natural language understanding framework into an argumentative dialogue system. |
| Outcome: | The proposed system is compared to a baseline system using a drop-down menu . the drop- down menu convinces, but the willingness to use it is significantly higher . |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
| Approach: | They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data. |
| Outcome: | The proposed approach improves model performance even in domain-shifted scenarios. |
SMEC:Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression (2025.emnlp-main)
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| Challenge: | Large language models generate high-dimensional embeddings that capture rich semantic and syntactic information. |
| Approach: | They propose a training framework to reduce dimensionality and complexity of large language models. |
| Outcome: | Experiments on image, text, and multimodal datasets show that the proposed training framework reduces dimensionality while maintaining performance. |