46 citations · 62 across the 15 of their papers we have counts for
15 papers
NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
In this study, we analyze automatic evaluation metrics for Natural Language Generation (NLG), specifically task-agnostic metrics and human-aligned metrics. Task-agnostic metrics, s…
A Survey for Efficient Open Domain Question Answering
Qin Zhang, Shangsi Chen, Dongkuan Xu +4
Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language pr…
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang +8
Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully…
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang +4
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…
Discourse-Aware Graph Networks for Textual Logical Reasoning
Yinya Huang, Lemao Liu, Kun Xu +3
Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations…
Phrase-level Textual Adversarial Attack with Label Preservation
Yibin Lei, Yu Cao, Dianqi Li +3
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.…
A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation
Yu Cao, Wei Bi, Meng Fang +2
Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue…
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection
Iftitahu Ni'mah, Meng Fang, Vlado Menkovski +1
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications. For example, intent classification in dialogue systems. The re…
DAGN: Discourse-Aware Graph Network for Logical Reasoning
Yinya Huang, Meng Fang, Yu Cao +2
Recent QA with logical reasoning questions requires passage-level relations among the sentences. However, current approaches still focus on sentence-level relations interacting amo…
Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation
Yu Cao, Liang Ding, Zhiliang Tian +1
Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore…
REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement
Yinya Huang, Meng Fang, Xunlin Zhan +3
When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commo…
Pretrained Language Models for Dialogue Generation with Multiple Input Sources
Yu Cao, Wei Bi, Meng Fang +1
Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to…
Unsupervised Domain Adaptation on Reading Comprehension
Yu Cao, Meng Fang, Baosheng Yu +1
Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these m…
A Corpus-free State2Seq User Simulator for Task-oriented Dialogue
Yutai Hou, Meng Fang, Wanxiang Che +1
Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotate…
BAG: Bi-directional Attention Entity Graph Convolutional Network for Multi-hop Reasoning Question Answering
Yu Cao, Meng Fang, Dacheng Tao
Multi-hop reasoning question answering requires deep comprehension of relationships between various documents and queries. We propose a Bi-directional Attention Entity Graph Convol…