10 citations · 28 across the 10 of their papers we have counts for
12 papers
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection
James Petullo, Sonny George, Dylan Cashman +1
A standard technique for scaling inference-time reasoning is Self-Consistency, whereby multiple candidate answers are sampled from an LLM and the most common answer is selected. Mo…
CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
James Petullo, Nianwen Xue
While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform well on the most challenging task…
Fact-checking AI-generated news reports: Can LLMs catch their own lies?
Jiayi Yao, Haibo Sun, Nianwen Xue
In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to de…
A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches
Annemarie Friedrich, Nianwen Xue, Alexis Palmer
Aspectual meaning refers to how the internal temporal structure of situations is presented. This includes whether a situation is described as a state or as an event, whether the si…
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
Jingxuan Yang, Kerui Xu, Jun Xu +5
In this paper, we present a neural model for joint dropped pronoun recovery (DPR) and conversational discourse parsing (CDP) in Chinese conversational speech. We show that DPR and…
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Jingxuan Yang, Kerui Xu, Jun Xu +5
Pronouns are often dropped in Chinese conversations and recovering the dropped pronouns is important for NLP applications such as Machine Translation. Existing approaches usually f…
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Jingxuan Yang, Jianzhuo Tong, Si Li +3
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering drop…
Neural Ranking Models for Temporal Dependency Structure Parsing
Yuchen Zhang, Nianwen Xue
We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a te…
Structured Interpretation of Temporal Relations
Yuchen Zhang, Nianwen Xue
Temporal relations between events and time expressions in a document are often modeled in an unstructured manner where relations between individual pairs of time expressions and ev…
Addressing the Data Sparsity Issue in Neural AMR Parsing
Xiaochang Peng, Chuan Wang, Daniel Gildea +1
Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue.…
Towards Accurate Word Segmentation for Chinese Patents
Si Li, Nianwen Xue
A patent is a property right for an invention granted by the government to the inventor. An invention is a solution to a specific technological problem. So patents often have a hig…
Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features
Attapol T. Rutherford, Vera Demberg, Nianwen Xue
Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not read…