Annual Meeting of the Association for Computational Linguistics · 412 papers
When Does Translation Require Context? A Data-driven, Multilingual Exploration
Patrick Fernandes (Carnegie Mellon University), Graham Neubig (Carnegie Mellon University)
CodeData-Centric LearningTransformerLarge Language ModelTextBenchmark
🎯 What it does: Propose the MuDA benchmark, which uses P-CXMI to identify context-dependent words in translation and automatically annotate five discourse phenomena, including lexical coherence, politeness forms, pronoun selection, verb forms, and ellipsis, constructing evaluation data for 14 language pairs.
🎯 What it does: Conduct unified experiments on Transformers and their efficient variants of self-attention across text, speech, and visual modalities, evaluating multiple efficiency metrics and determining when to use them.
WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models
Virginia Felkner, Jonathan May (Information Sciences Institute University of Southern California)
CodeTransformerLarge Language ModelSupervised Fine-TuningTextBenchmark
🎯 What it does: Construct the WinoQueer benchmark through community surveys to evaluate negative biases of large language models toward the LGBTQ+ community;
Won’t Get Fooled Again: Answering Questions with False Premises
Shengding Hu (Tsinghua University), Maosong Sun (Tsinghua University)
CodeExplainability and InterpretabilityTransformerLarge Language ModelSupervised Fine-TuningPrompt EngineeringText
🎯 What it does: This paper studies the robustness of pre-trained language models in answering questions with false premises (FPQ) and proposes a method to activate the model's rebuttal capability;
CodeRepresentation LearningMeta LearningTransformerLarge Language ModelTextChain-of-Thought
🎯 What it does: Proposed the Word Sense Extension (WSE) paradigm, simulating how language users create new meanings for words in novel contexts, and constructed a training framework based on semantic partitioning;
XDailyDialog: A Multilingual Parallel Dialogue Corpus
Zeming Liu (Harbin Institute of Technology), Kaiping Peng (Tsinghua University)
CodeGenerationData SynthesisRetrievalTransformerLarge Language ModelTextRetrieval-Augmented Generation
🎯 What it does: This paper constructs the first publicly available multilingual parallel open-domain dialogue corpus, XDailyDialog, and proposes three dialogue tasks: multilingual, cross-lingual, and multilingual mixed.
🎯 What it does: This paper proposes the MetaEvent framework for zero-shot and few-shot event detection, enabling the model to quickly adapt to unknown event types through meta-learning.
🎯 What it does: Propose a zero-shot and few-shot stance detection framework based on conditional generation, which uses templates to denoise and generate complete sentences for stance determination;
Kung-Hsiang Huang (University of Illinois Urbana-Champaign), Heng Ji (University of Illinois Urbana-Champaign)
CodeDomain AdaptationExplainability and InterpretabilityData-Centric LearningTransformerLarge Language ModelSupervised Fine-TuningTextBiomedical DataRetrieval-Augmented Generation
🎯 What it does: Proposes a zero-shot fact error correction framework ZEROFEC, which corrects factual errors under given evidence using question answering and document-level entailment assessment, while maintaining faithfulness to the original sentence.