Papers by Tongtong Liu
Towards relation extraction from speech (2022.emnlp-main)
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| Challenge: | Existing methods for extracting relations from speech have been neglected due to the nature of speech. |
| Approach: | They propose a listening information extraction task that uses speech to extract relation extraction from speech . they use a text-to-speech system and crowd-sourced native English speakers to test the task . |
| Outcome: | The proposed task extracts semantic relationships from speech data using a new model . the proposed task is more challenging than the existing method due to the characteristics of speech . |
MPO: Multilingual Safety Alignment via Reward Gap Optimization (2025.acl-long)
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Weixiang Zhao, Yulin Hu, Yang Deng, Tongtong Wu, Wenxuan Zhang, Jiahe Guo, An Zhang, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu
| Challenge: | Existing preference learning methods for safety alignment are monolingual and struggle with noisy multilingual data. |
| Approach: | They propose a multilingual reward gaP optimization approach that leverages the well-aligned safety capabilities of the dominant language to improve safety alignment across multiple languages. |
| Outcome: | Extensive experiments on three LLMs, LLaMA-3.1, Gemma-2 and Qwen2.5, validate MPO’s efficacy in multilingual safety alignment without degrading general multilingual utility. |
Multi-stage Pre-training over Simplified Multimodal Pre-training Models (2021.acl-long)
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| Challenge: | Existing multimodal pre-training models require large amounts of training data and have huge model sizes, making them impossible to apply in low-resource situations. |
| Approach: | They propose a multi-stage pre-training method which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages. |
| Outcome: | The proposed method outperforms the original model in Image-Text Retrieval task and outperformed the original LXMERT model in downstream tasks. |
Real-time Ad Retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising (2025.emnlp-main)
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| Challenge: | Existing methods for retrieving documents and ads use one-to-few mappings and time-consuming content extraction. |
| Approach: | They propose a framework that leverages LLM-generated commercial intents as an intermediate semantic representation to directly retrieve ads for queries in real-time. |
| Outcome: | The proposed framework has been implemented in a real-world online system, handling daily search volumes in billions. |
A Simple Contrastive Learning Framework for Interactive Argument Pair Identification via Argument-Context Extraction (2022.emnlp-main)
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| Challenge: | Existing work on argument mining uses context-based methods to identify whether two arguments are interactively related. |
| Approach: | They propose a contrastive learning framework to extract valuable information from the context. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the benchmark dataset and visually displays more compact representations. |