39 citations · 79 across the 15 of their papers we have counts for
13 papers · 1 filter
Explicit Interaction Network for Aspect Sentiment Triplet Extraction
Peiyi Wang, Tianyu Liu, Damai Dai +3
Aspect Sentiment Triplet Extraction (ASTE) aims to recognize targets, their sentiment polarities and opinions explaining the sentiment from a sentence. ASTE could be naturally divi…
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Runxin Xu, Tianyu Liu, Lei Li +1
Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the tar…
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Tianyu Liu, Yizhe Zhang, Chris Brockett +4
Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications. Exist…
Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View
Tianyu Liu, Xin Zheng, Baobao Chang +1
In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record. We thus…
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Lianzhe Huang, Peiyi Wang, Sujian Li +5
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize…
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference
Tianyu Liu, Xin Zheng, Xiaoan Ding +2
The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust. In this paper, we focus…