1.2k citations · 2.7k across the 54 of their papers we have counts for
54 papers
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
Yihao Fang, Xianzhi Li, Stephen W. Thomas +1
Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text. Despite the progress mad…
NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic
Zi'ou Zheng, Xiaodan Zhu
Reasoning has been a central topic in artificial intelligence from the beginning. The recent progress made on distributed representation and neural networks continues to improve th…
Prototype-Based Interpretability for Legal Citation Prediction
Chu Fei Luo, Rohan Bhambhoria, Samuel Dahan +1
Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such…
Prefix Propagation: Parameter-Efficient Tuning for Long Sequences
Jonathan Li, Will Aitken, Rohan Bhambhoria +1
Parameter-efficient tuning aims to mitigate the large memory requirements of adapting pretrained language models for downstream tasks. For example, one popular method, prefix-tunin…
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data
Stephen Obadinma, Hongyu Guo, Xiaodan Zhu
Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is compar…
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Stephen Obadinma, Faiza Khan Khattak, Shirley Wang +23
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natura…
Parameter-Efficient Legal Domain Adaptation
Jonathan Li, Rohan Bhambhoria, Xiaodan Zhu
Seeking legal advice is often expensive. Recent advancements in machine learning for solving complex problems can be leveraged to help make legal services more accessible to the pu…
JECC: Commonsense Reasoning Tasks Derived from Interactive Fictions
Mo Yu, Yi Gu, Xiaoxiao Guo +5
Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an essential cornerstone in building general AI systems. We propose a new…
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
Yufei Feng, Xiaoyu Yang, Xiaodan Zhu +1
We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through po…
Exploring Decomposition for Table-based Fact Verification
Xiaoyu Yang, Xiaodan Zhu
Fact verification based on structured data is challenging as it requires models to understand both natural language and symbolic operations performed over tables. Although pre-trai…
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
Lei Shen, Haolan Zhan, Xin Shen +3
Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better re…
Unsupervised Pre-training with Structured Knowledge for Improving Natural Language Inference
Xiaoyu Yang, Xiaodan Zhu, Zhan Shi +1
While recent research on natural language inference has considerably benefited from large annotated datasets, the amount of inference-related knowledge (including commonsense) prov…
Detecting Speaker Personas from Conversational Texts
Jia-Chen Gu, Zhen-Hua Ling, Yu Wu +3
Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue,…
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking
Yinpei Dai, Hangyu Li, Yongbin Li +4
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use cu…
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning
Boyuan Zheng, Xiaoyu Yang, Yu-Ping Ruan +4
This paper introduces the SemEval-2021 shared task 4: Reading Comprehension of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the ability of machines in re…
Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots
Jia-Chen Gu, Hui Liu, Zhen-Hua Ling +3
Persona can function as the prior knowledge for maintaining the consistency of dialogue systems. Most of previous studies adopted the self persona in dialogue whose response was ab…
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning
Hui Liu, Danqing Zhang, Bing Yin +1
Exploiting label hierarchies has become a promising approach to tackling the zero-shot multi-label text classification (ZS-MTC) problem. Conventional methods aim to learn a matchin…
Improving Text-to-SQL with Schema Dependency Learning
Binyuan Hui, Xiang Shi, Ruiying Geng +4
Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on t…
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Binyuan Hui, Ruiying Geng, Qiyu Ren +7
Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. C…
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
Chao-Hong Tan, Xiaoyu Yang, Zi'ou Zheng +7
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to genera…
Exploring End-to-End Differentiable Natural Logic Modeling
Yufei Feng, Zi'ou Zheng, Quan Liu +2
We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natura…
Deriving Commonsense Inference Tasks from Interactive Fictions
Mo Yu, Xiaoxiao Guo, Yufei Feng +3
Commonsense reasoning simulates the human ability to make presumptions about our physical world, and it is an indispensable cornerstone in building general AI systems. We propose a…
Program Enhanced Fact Verification with Verbalization and Graph Attention Network
Xiaoyu Yang, Feng Nie, Yufei Feng +3
Performing fact verification based on structured data is important for many real-life applications and is a challenging research problem, particularly when it involves both symboli…
SemEval-2020 Task 4: Commonsense Validation and Explanation
Cunxiang Wang, Shuailong Liang, Yili Jin +3
In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a…