Xiaodan Zhu

SemEval-2020 Task 4: Commonsense Validation and Explanation

2020

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 natural language statement that makes sense to humans from …

SemEval-2020 Task 5: Counterfactual Recognition

2020

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the …

Detecting Speaker Personas from Conversational Texts

2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Personas are useful for dialogue response prediction.

Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support

2022

Stephen Obadinma, Faiza Khan Khattak, Shirley Wang, Tania Sidhorn, Elaine Lau, Sean Robertson, Jingcheng Niu, Winnie Au, Alif Munim, Karthik Raja Kalaiselvi Bhaskar. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: …

SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models

2024 · arXiv (Cornell University)

Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support …

On the Robustness of Verbal Confidence of LLMs in Adversarial Attacks

2025

Robust verbal confidence generated by large language models (LLMs) is crucial for the deployment of LLMs to help ensure transparency, trust, and safety in many applications, including those involving human-AI interactions. In this paper, we …

Long Short-Term Memory Over Recursive Structures

2015 · NPARC

The chain-structured long short-term memory (LSTM) has showed to be effective in a wide range of problems such as speech recognition and machine translation. In this paper, we pro-pose to extend it to tree structures, …

Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference.

2016 · arXiv (Cornell University)

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is notoriously challenging but is fundamental to natural language understanding and many applications. With the availability of large annotated data, …

Distraction-based neural networks for modeling documents

2016 · International Joint Conference on Artificial Intelligence

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences. Whether and how such an approach can be extended …

Enhanced LSTM for Natural Language Inference

2017

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data In this paper, we present a new state-of-the-art result, achieving …

Neural Natural Language Inference Models Enhanced with External Knowledge

2018

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve …