Papers by Viet Lai
BizBench: A Quantitative Reasoning Benchmark for Business and Finance (2024.acl-long)
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| Challenge: | Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. |
| Approach: | They propose a benchmark for evaluating models’ ability to reason about realistic financial problems by focusing on question-answering over financial data via program synthesis. |
| Outcome: | The proposed benchmark evaluates models' financial background knowledge, ability to parse financial documents, and capacity to solve complex problems with code. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
DocFinQA: A Long-Context Financial Reasoning Dataset (2024.acl-short)
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| Challenge: | Existing work on automating financial numerical reasoning focuses on unrealistically specific document snippets, failing to reflect the broader and more realistic scenarios faced by analysts. |
| Approach: | They propose a long-document financial QA task that augments 7,437 questions from existing FinQA dataset with full-document context, extending the average context length from under 700 words in FinQA to 123k words in DocFinQA. |
| Outcome: | The proposed task extends the average context length from under 700 words in FinQA to 123k words in DocFinQA. |
BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript (2022.findings-naacl)
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| Challenge: | a growing number of livestreaming videos provide useful knowledge with exceptional visual demonstrations. |
| Approach: | They propose a human-annotated corpus for punctuation restoration in livestreaming video transcripts . they show popular natural language processing tools underperform on sentence boundary detection . |
| Outcome: | The proposed dataset shows that natural language processing tools underperform on sentence boundary detection on livestreaming video transcripts. |
Unleash GPT-2 Power for Event Detection (2021.acl-long)
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| Challenge: | Event Detection (ED) aims to recognize mentions of events and their types in text. |
| Approach: | They propose to exploit a pre-trained language model to generate training samples for ED. |
| Outcome: | The proposed model improves on multiple ED benchmark datasets and establishes state-of-the-art results. |
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback (2023.emnlp-demo)
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| Challenge: | Existing instruction-tuned open-source LLMs have only been instruction- tuned for English and a few popular languages, thus hindering their accessibility to many other languages in the world. |
| Approach: | They propose a framework that uses supervised fine-tuning and reinforcement learning from human feedback to improve the accessibility of large language models. |
| Outcome: | The proposed framework enables the evaluation of generative LLMs in multiple languages. |
Learning Prototype Representations Across Few-Shot Tasks for Event Detection (2021.emnlp-main)
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| Challenge: | Existing training data for event detection are too expensive to achieve in real applications where novel event types emerge . Typical ED systems require labeled data for each predefined event type, but only a few examples are available. |
| Approach: | They propose to introduce cross-task prototypes to model relationships between training tasks in few-shot learning for event detection. |
| Outcome: | The proposed model improves on three few-shot learning datasets. |
BehanceCC: A ChitChat Detection Dataset For Livestreaming Video Transcripts (2022.lrec-1)
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| Challenge: | livestreaming videos contain a considerable amount of off-topic content, causing noises and data load to downstream applications. |
| Approach: | They propose a human-annotated benchmark dataset for off-topic detection in livestreaming video transcripts. |
| Outcome: | The proposed dataset reveals the complexity of chitchat detection in livestreaming videos . livestreams tend to be longer than pre-recorded videos and have fewer verbal pauses . |
MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)
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| Challenge: | Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English. |
| Approach: | They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages. |
| Outcome: | The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters . |
An Analysis of Multilingual FActScore (2024.emnlp-main)
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| Challenge: | Recent advances in LLMs have demonstrated significant capabilities in many applications. |
| Approach: | They propose a dataset for FActScore on texts generated by strong multilingual LLMs and evaluate their performance in other languages. |
| Outcome: | The proposed dataset shows that LLMs exhibit distinct behaviors in fact extraction and fact scoring tasks. |
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning (2023.findings-emnlp)
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| Challenge: | Recent advances in natural language processing (NLP) have led to significant breakthroughs in the field. |
| Approach: | They evaluate ChatGPT over multiple tasks with diverse languages and large datasets to provide more comprehensive information for multilingual NLP applications. |
| Outcome: | The proposed model can process and generate texts for multiple languages due to its multilingual training data. |
Event Detection for Suicide Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue . |
| Approach: | They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation . |
| Outcome: | The proposed dataset captures suicide actions and ideation, and general risk and protective factors. |
BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts (2022.lrec-1)
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| Challenge: | Question-Answer (QA) is an effective method for storing knowledge . prior QA identification systems have been limited to formal written documents . a large-scale QA dataset annotated by human over 500 hours of video transcripts is a challenge . |
| Approach: | They present a large-scale QA identification dataset annotated by human over 500 hours of video transcripts. |
| Outcome: | The proposed dataset presents unique challenges for existing methods . it shows that the annotated dataset presents challenges for new methods - the results will be released . |