EMNLP

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

James A. Michaelov, Catherine Arnett, Tyler A. Chang, Benjamin K. Bergen

Comments Accepted at EMNLP 2023

Data Similarity is Not Enough to Explain Language Model Performance

Gregory Yauney, Emily Reif, David Mimno

Journal ref Published in EMNLP 2023

SentAlign: Accurate and Scalable Sentence Alignment

Steinþór Steingrímsson, Hrafn Loftsson, Andy Way

Comments EMNLP 2023 System Demonstration paper

Large Language Models are legal but they are not: Making the case for a powerful LegalLLM

Thanmay Jayakumar, Fauzan Farooqui, Luqman Farooqui

Comments 7 pages, Accepted at Natural Legal Language Processing Workshop, EMNLP 2023

Accelerating Toeplitz Neural Network with Constant-time Inference Complexity

Zhen Qin, Yiran Zhong

Comments Accepted to EMNLP 2023. Yiran Zhong is the corresponding author. The source code is available at https://github.com/OpenNLPLab/ETSC-Exact-Toeplitz-to-SSM-Conversion

MedEval: A Multi-Level, Multi-Task, and Multi-Domain Medical Benchmark for Language Model Evaluation

Zexue He, Yu Wang, An Yan, Yao Liu, Eric Y. Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

Comments Accepted to EMNLP 2023. Camera-ready version: updated IRB, added more evaluation results on LLMs such as GPT4, LLaMa2, and LLaMa2-chat

VIBE: Topic-Driven Temporal Adaptation for Twitter Classification

Yuji Zhang, Jing Li, Wenjie Li

Comments accepted by EMNLP 2023

Journal ref The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

Selectively Answering Ambiguous Questions

Jeremy R. Cole, Michael J. Q. Zhang, Daniel Gillick, Julian Martin Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein

Comments To appear in EMNLP 2023. 9 pages, 5 figures, 2 pages of appendix

Evaluating and Modeling Attribution for Cross-Lingual Question Answering

Benjamin Muller, John Wieting, Jonathan H. Clark, Tom Kwiatkowski, Sebastian Ruder, Livio Baldini Soares, Roee Aharoni, Jonathan Herzig, Xinyi Wang

Comments Published as a long paper at EMNLP 2023

Schema-adaptable Knowledge Graph Construction

Hongbin Ye, Honghao Gui, Xin Xu, Xi Chen, Huajun Chen, Ningyu Zhang

Comments EMNLP 2023 (Findings)

Retrieve and Copy: Scaling ASR Personalization to Large Catalogs

Sai Muralidhar Jayanthi, Devang Kulshreshtha, Saket Dingliwal, Srikanth Ronanki, Sravan Bodapati

Comments EMNLP 2023

On Using Distribution-Based Compositionality Assessment to Evaluate Compositional Generalisation in Machine Translation

Anssi Moisio, Mathias Creutz, Mikko Kurimo

Comments To appear at the GenBench Workshop at EMNLP 2023

Automated Fact-Checking in Dialogue: Are Specialized Models Needed?

Eric Chamoun, Marzieh Saeidi, Andreas Vlachos

Comments Accepted to EMNLP 2023

TempTabQA: Temporal Question Answering for Semi-Structured Tables

Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar

Comments EMNLP 2023(Main), 23 Figures, 32 Tables

How Well Do Text Embedding Models Understand Syntax?

Yan Zhang, Zhaopeng Feng, Zhiyang Teng, Zuozhu Liu, Haizhou Li

Comments Accepted to EMNLP-Findings 2023, datasets and code are released

Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources

Yerin Hwang, Yongil Kim, Hyunkyung Bae, Jeesoo Bang, Hwanhee Lee, Kyomin Jung

Comments Accepted to EMNLP 2023 main conference

Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering

Sukmin Cho, Jeongyeon Seo, Soyeong Jeong, Jong C. Park

Comments Findings of EMNLP 2023 Camera Ready

PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering

Wookje Han, Jinsol Park, Kyungjae Lee

Comments 11 pages 3 figures, Accepted to EMNLP 2023 (short)

Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification

Mujeen Sung, James Gung, Elman Mansimov, Nikolaos Pappas, Raphael Shu, Salvatore Romeo, Yi Zhang, Vittorio Castelli

Comments EMNLP 2023

Larger Probes Tell a Different Story: Extending Psycholinguistic Datasets Via In-Context Learning

Namrata Shivagunde, Vladislav Lialin, Anna Rumshisky

Comments 14 pages, 6 figures. Published as a conference paper at EMNLP 2023 (short). The datasets and code are available on this $\href{https://github.com/text-machine-lab/extending_psycholinguistic_dataset}{URL}$

Consistency Analysis of ChatGPT

Myeongjun Erik Jang, Thomas Lukasiewicz

Comments 15 pages

Journal ref The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

LM-Polygraph: Uncertainty Estimation for Language Models

Ekaterina Fadeeva, Roman Vashurin, Akim Tsvigun, Artem Vazhentsev, Sergey Petrakov, Kirill Fedyanin, Daniil Vasilev, Elizaveta Goncharova, Alexander Panchenko, Maxim Panov, Timothy Baldwin, Artem Shelmanov

Comments Accepted at EMNLP-2023

calamanCy: A Tagalog Natural Language Processing Toolkit

Lester James V. Miranda

Comments To be published in The Third Workshop for NLP-OSS at EMNLP 2023

Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations

Zilu Tang, Mayank Agarwal, Alex Shypula, Bailin Wang, Derry Wijaya, Jie Chen, Yoon Kim

Comments 9 pages, 4 figures, 5 tables, 48 pages total. To be published in EMNLP Findings 2023

Citance-Contextualized Summarization of Scientific Papers

Shahbaz Syed, Ahmad Dawar Hakimi, Khalid Al-Khatib, Martin Potthast

Comments Accepted at EMNLP 2023 Findings

StyleBART: Decorate Pretrained Model with Style Adapters for Unsupervised Stylistic Headline Generation

Hanqing Wang, Yajing Luo, Boya Xiong, Guanhua Chen, Yun Chen

Comments Findings of EMNLP 2023

Enhancing Low-resource Fine-grained Named Entity Recognition by Leveraging Coarse-grained Datasets

Su Ah Lee, Seokjin Oh, Woohwan Jung

Comments Accepted to EMNLP 2023

Task-Adaptive Tokenization: Enhancing Long-Form Text Generation Efficacy in Mental Health and Beyond

Siyang Liu, Naihao Deng, Sahand Sabour, Yilin Jia, Minlie Huang, Rada Mihalcea

Comments Accepted at the main conference of The 2023 Conference on Empirical Methods in Natural Language Processing; 8 pages

Journal ref The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

An Investigation of LLMs' Inefficacy in Understanding Converse Relations

Chengwen Qi, Bowen Li, Binyuan Hui, Bailin Wang, Jinyang Li, Jinwang Wu, Yuanjun Laili

Comments Accepted by EMNLP 2023

Evaluation of African American Language Bias in Natural Language Generation

Nicholas Deas, Jessi Grieser, Shana Kleiner, Desmond Patton, Elsbeth Turcan, Kathleen McKeown

Comments EMNLP 2023 Camera-Ready