11 citations · 32 across the 12 of their papers we have counts for
12 papers · 1 filter
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification
Yingjie Zhu, Jiasheng Si, Yibo Zhao +3
Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain…
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources
Jiasheng Si, Yingjie Zhu, Xingyu Shi +2
Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring b…
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
Chenduo Hao, Xu Zhang, Chuanbao Gao +1
The NL2SQL task involves parsing natural language statements into SQL queries. While most state-of-the-art methods treat NL2SQL as a slot-filling task and use feature representatio…
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning
Jiasheng Si, Yingjie Zhu, Deyu Zhou
The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the…
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Zeng Yang, Linhai Zhang, Deyu Zhou
Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets i…
Topic-Aware Evidence Reasoning and Stance-Aware Aggregation for Fact Verification
Jiasheng Si, Deyu Zhou, Tongzhe Li +2
Fact verification is a challenging task that requires simultaneously reasoning and aggregating over multiple retrieved pieces of evidence to evaluate the truthfulness of a claim. E…