Papers by Timothy Liu
Narrative Modeling with Memory Chains and Semantic Supervision (P18-2)
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| Challenge: | Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. |
| Approach: | They propose a method that tracks various semantic aspects with external neural memory chains . they propose to encourage each to focus on a particular semantic aspect . |
| Outcome: | The proposed method outperforms baselines on the task of story ending prediction. |
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis (N18-2)
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| Challenge: | Recent work on target-dependent biLSTMs has shown that they are ineffective in aspect-based sentiment analysis. |
| Approach: | They propose a novel architecture that uses external memory chains with a delayed memory update mechanism to track entities. |
| Outcome: | The proposed model improves on a TABSA task using external memory chains with a delayed memory update mechanism. |
Towards Better Characterization of Paraphrases (2022.acl-long)
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| Challenge: | Existing models of natural language processing lack generalization and performance . existing models are often overreliant on learned spurious correlations resulting in poor generalization. |
| Approach: | They propose to use word position deviation and lexical deviation to characterize paraphrase pairs without expert human annotation. |
| Outcome: | The proposed metrics improve generalizability of models trained on the dataset and can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings (2024.naacl-long)
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| Challenge: | Large language models (LLMs) are adept at question answering and reasoning tasks, but when reasoning in situational context, human expectations vary depending on the relevant cultural common ground. |
| Approach: | They construct and evaluate a dataset for proverb understanding with conversational context for six different languages and their usage within the context. |
| Outcome: | The proposed model is able to reason with proverbs and sayings in conversational contexts. |
NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines (2023.findings-acl)
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| Challenge: | Popular datasets used for metaphor processing tasks were constructed from dated source texts. |
| Approach: | They propose a large contemporary dataset of news headlines hand-annotated with metaphorical verbs. |
| Outcome: | The proposed dataset includes headlines from political, satirical, reliable and fake sources. |
ESRA: Explainable Scientific Research Assistant (2021.acl-demo)
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Pollawat Hongwimol, Peeranuth Kehasukcharoen, Pasit Laohawarutchai, Piyawat Lertvittayakumjorn, Aik Beng Ng, Zhangsheng Lai, Timothy Liu, Peerapon Vateekul
| Challenge: | Existing literature search systems only present metadata of papers as search results, which requires users to read the entire abstracts to understand the brief contents of the returned papers. |
| Approach: | They propose to use a knowledge graph extracted from abstracts of 23k papers on arXiv’s cs.CL category to augment search results with relevant details and explanations. |
| Outcome: | The proposed platform can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest. |
IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning (2026.acl-long)
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Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand, Chhavi Kirtani, Erik Cambria, Zhengchen Zhang, Zhengkui Wang, Timothy Liu, Aik Beng Ng, Simon See, Rajiv Ratn Shah
| Challenge: | Curriculum learning fails to generate consistent step-by-step reasoning in multilingual and low-resource settings. |
| Approach: | They propose a framework that combines supervised fine-tuning with reverse curriculum reinforcement learning to generate consistent step-by-step reasoning. |
| Outcome: | The proposed framework outperforms single-axis benchmarks and multilingual test sets on math reasoning tasks and in high-resource languages. |
MeetingBank: A Benchmark Dataset for Meeting Summarization (2023.acl-long)
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| Challenge: | a lack of annotated meeting corpora hinders the development of meeting summarization technology. |
| Approach: | They present a new benchmark dataset of city council meetings over the past decade . they use a divide-and-conquer approach to divide professionally written minutes into shorter passages . |
| Outcome: | The proposed dataset provides a testbed for various meeting summarization systems and allows the public to gain insight into how council decisions are made. |