1 code implementation • 24 Feb 2024 • Zhaoyue Sun, Gabriele Pergola, Byron C. Wallace, Yulan He
With the advent of large language models (LLMs), there has been growing interest in exploring their potential for medical applications.
1 code implementation • 16 Aug 2023 • Junru Lu, Siyu An, Mingbao Lin, Gabriele Pergola, Yulan He, Di Yin, Xing Sun, Yunsheng Wu
We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations.
1 code implementation • 8 May 2023 • Junru Lu, Gabriele Pergola, Lin Gui, Yulan He
In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting.
1 code implementation • 11 Feb 2023 • Junru Lu, Jiazheng Li, Byron C. Wallace, Yulan He, Gabriele Pergola
In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.
no code implementations • 10 Feb 2023 • Xingwei Tan, Gabriele Pergola, Yulan He
Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge.
1 code implementation • 24 Oct 2022 • Junru Lu, Xingwei Tan, Gabriele Pergola, Lin Gui, Yulan He
Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines.
1 code implementation • 22 Oct 2022 • Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron C. Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.
1 code implementation • NAACL 2022 • Lixing Zhu, Zheng Fang, Gabriele Pergola, Rob Procter, Yulan He
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • EMNLP 2021 • Xingwei Tan, Gabriele Pergola, Yulan He
Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.
1 code implementation • ACL 2021 • Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He
To investigate the degree of reliance of existing ECE models on clause relative positions, we propose a novel strategy to generate adversarial examples in which the relative position information is no longer the indicative feature of cause clauses.
1 code implementation • ACL 2021 • Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He
Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states.
Ranked #12 on Emotion Recognition in Conversation on DailyDialog
no code implementations • EACL 2021 • Gabriele Pergola, Elena Kochkina, Lin Gui, Maria Liakata, Yulan He
Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.
no code implementations • EACL 2021 • Runcong Zhao, Lin Gui, Gabriele Pergola, Yulan He
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews.
1 code implementation • COLING 2020 • Junru Lu, Gabriele Pergola, Lin Gui, Binyang Li, Yulan He
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation.
1 code implementation • NAACL 2021 • Gabriele Pergola, Lin Gui, Yulan He
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTMs).
no code implementations • 22 Nov 2019 • Gabriele Pergola, Yulan He, David Lowe
Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus.
no code implementations • IJCNLP 2019 • Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He
In recent years, advances in neural variational inference have achieved many successes in text processing.
no code implementations • 18 Aug 2019 • Gabriele Pergola, Lin Gui, Yulan He
We propose a topic-dependent attention model for sentiment classification and topic extraction.