Findings of the Association for Computational Linguistics: EMNLP 2020

By Trevor Cohn, Yulan He, Yang Liu. 2020

270 citations recorded by the source corpus.

A BERT-based Distractor Generation Scheme with Multi-tasking and Negative Answer Training Strategies

Subfields: Artificial Intelligence

Citations in the corpus

• Survey of Hallucination in Natural Language Generation (2022, neutral): “However, the correctness of numerals in generated text, such as date, quantities and scalars are important for readers [180, 230, 234].”
• Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts (2023, unknown): “This excitement around LLMs and prompting is propelling a rapidly growing set of LLM-powered applications [23] and prompt design tools [3, 20, 32].”
• When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53,000+ legal holdings (2021, unknown): “[7] explored the effects of law pretraining using various strategies and evaluate on a broader range of legal NLP tasks.”
• Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey (2021, unknown): “g 108, 117] or another argument that was previously identiied [97] (see the Event Extraction row in Table 4).”
• COLD: A Benchmark for Chinese Offensive Language Detection (2022, unknown): “, 2020] and RealToxicPrompts [Gehman et al., 2020]) for measuring the language toxicity.”
• Harms from Increasingly Agentic Algorithmic Systems (2023, unknown): “…and Gebru 2018; Ehsan et al. 2022; Kasy and Abebe 2021; steven t. piantadosi [@spiantado] 2022; Wolfe and Caliskan 2022], the generation of toxic language [Abid et al. 2021; Gehman et al. 2020], and informational harms [Carroll et al. 2022; Jiang et al. 2019; Li et al. 2022; Weidinger et al. 2022].”
• CVEfixes: automated collection of vulnerabilities and their fixes from open-source software (2021, unknown): “, and cover widelyused programming languages [5, 6].”
• Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models (2021, unknown): “This is especially crucial for the robustness evaluation, as some adversarial examples generated by automatic attack algorithms can fool humans as well [34].”
• Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection (2022, unknown): “GPT-3 is also prone to producing hate speech (Gehman et al., 2020) and misinformation (McGuffie and Newhouse, 2020), which we would expect if its quality filter fails to distinguish the factual reliability of news sources in its training data (§4).”
• The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey (2021, unknown): “, 2020] Weakly-Supervised-based CNN/DM X HERMAN [Zhao et al., 2020] Weakly-Supervised-based XSum Zhou et al.”
• Building Machine Translation Systems for the Next Thousand Languages (2022, unknown): “…datasets and models significantly reduces the amount of data and research efforts needed to build practical NLP technologies for these languages (Wang et al., 2020; Emezue & Dossou, 2021; Adelani et al., 2022; Alabi et al., 2022; Nekoto et al., 2022), and research efforts could be more…”