9 citations · 26 across the 17 of their papers we have counts for
13 papers · 1 filter
Intra-Prompt Parallel Decoding for Common-Context Question Answering
Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani +3
In common-context question answering (CCQA) tasks, multiple input questions share a common context to base their answers from. However, Large Language Models typically generate eac…
Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction
Theodore Glavas, Nikhita Vedula, Dushyanta Dhyani +2
Some text generation tasks, such as Attribute Value Extraction (AVE), require decoding multiple independent sequences from the same document context. While standard autoregressive…
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains
Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti +6
Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data i…
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution
Yilun Zhu, Siyao Peng, Sameer Pradhan +1
Singleton mentions, i.e.~entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incor…
Can Large Language Models Understand Context?
Yilun Zhu, Joel Ruben Antony Moniz, Shruti Bhargava +6
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. Howe…
Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization
Yilun Zhu, Siyao Peng, Sameer Pradhan +1
Previous attempts to incorporate a mention detection step into end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention span data as…
GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation
Tatsuya Aoyama, Shabnam Behzad, Luke Gessler +6
We present GENTLE, a new mixed-genre English challenge corpus totaling 17K tokens and consisting of 8 unusual text types for out-of domain evaluation: dictionary entries, esports c…
Findings of the Shared Task on Multilingual Coreference Resolution
Zdeněk Žabokrtský, Miloslav Konopík, Anna Nedoluzhko +7
This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop…
Anatomy of OntoGUM--Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms
Yilun Zhu, Sameer Pradhan, Amir Zeldes
SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it d…
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection
Luke Gessler, Shabnam Behzad, Yang Janet Liu +3
This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification. Our system, called DisCoDisCo,…
OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres
Yilun Zhu, Sameer Pradhan, Amir Zeldes
SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it d…
AMALGUM -- A Free, Balanced, Multilayer English Web Corpus
Luke Gessler, Siyao Peng, Yang Liu +3
We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency t…
Adpositional Supersenses for Mandarin Chinese
Yilun Zhu, Yang Liu, Siyao Peng +3
This study adapts Semantic Network of Adposition and Case Supersenses (SNACS) annotation to Mandarin Chinese and demonstrates that the same supersense categories are appropriate fo…