EMNLP 2023 Conference Submissions

CodeFusion: A Pre-trained Diffusion Model for Code Generation
Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Suzana Negreanu, Gust Verbruggen
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Visually Grounded Continual Language Learning with Selective Specialization
Kyra Ahrens, Lennart Bengtson, Jae Hee Lee, Stefan Wermter
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Xinyu Wang, Lin Gui, Yulan He
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings
Andrea W Wen-Yi, David Mimno
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Self-Supervised Behavior Cloned Transformers are Path Crawlers for Text Games
Ruoyao Wang, Peter Jansen
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix
Kuan-Hao Huang, Liang Tan, Rui Hou, Sinong Wang, Amjad Almahairi, Ruty Rinott
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Seq2seq is All You Need for Coreference Resolution
Wenzheng Zhang, Sam Wiseman, Karl Stratos
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Co$^2$PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Xiangjue Dong, Ziwei Zhu, Zhuoer Wang, Maria Teleki, James Caverlee
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Argument mining as a multi-hop generative machine reading comprehension task
Boyang Liu, Viktor Schlegel, Riza Batista-Navarro, Sophia Ananiadou
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
Yinghan Long, Sayeed Shafayet Chowdhury, Kaushik Roy
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Consonant is all you need: a compact representation of English text for efficient NLP
Maged S. Al-shaibani, Irfan Ahmad
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Dynamic Low-rank Estimation for Transformer-based Language Models
Ting Hua, Xiao Li, Shangqian Gao, Yen-Chang Hsu, Yilin Shen, Hongxia Jin
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Selectively Answering Ambiguous Questions
Jeremy R. Cole, Michael JQ Zhang, Daniel Gillick, Julian Martin Eisenschlos, Bhuwan Dhingra, Jacob Eisenstein
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
HiddenTables and PyQTax: A Cooperative Game and Dataset For TableQA to Ensure Scale and Data Privacy Across a Myriad of Taxonomies
William Watson, Nicole Cho, Tucker Balch, Manuela Veloso
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
What Else Do I Need to Know? The Effect of Background Information on Users’ Reliance on QA Systems
Navita Goyal, Eleftheria Briakou, Amanda Stephanie Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare R. Voss, Marine Carpuat, Hal Daumé III
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
C-STS: Conditional Semantic Textual Similarity
Ameet Deshpande, Carlos E Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, Karthik R Narasimhan
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages
Asım Ersoy, Gerson Vizcarra, Tahsin Mayeesha, Benjamin Muller
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research
Dimosthenis Antypas, Asahi Ushio, Francesco Barbieri, Leonardo Neves, Kiamehr Rezaee, Luis Espinosa-Anke, Jiaxin Pei, Jose Camacho-Collados
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Role of Context in Unsupervised Sentence Representation Learning: the Case of Dialog Act Modeling
Rastislav Hronsky, Emmanuel Keuleers
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Robustness of Named-Entity Replacements for In-Context Learning
Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization
Dhruv Mehra, Lingjue Xie, Ella Hofmann-Coyle, Mayank Kulkarni, Daniel Preotiuc-Pietro
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?
Sathvik Nair, Philip Resnik
- Published: 23 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Findings
- Readers: Everyone
Enabling Large Language Models to Generate Text with Citations
Tianyu Gao, Howard Yen, Jiatong Yu, Danqi Chen
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA
David Heineman, Yao Dou, Mounica Maddela, Wei Xu
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone
$k$NN-LM Does Not Improve Open-ended Text Generation
Shufan Wang, Yixiao Song, Andrew Drozdov, Aparna Garimella, Varun Manjunatha, Mohit Iyyer
- Published: 07 Oct 2023, Last Modified: 01 Dec 2023
- EMNLP 2023 Main
- Readers: Everyone