Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

By Kevin Knight, Ani Nenkova, Owen Rambow. North American Chapter of the Association for Computational Linguistics, 2016

359 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/3779c1d2059e0daa9b511bb354172a9726cce54b

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In Artificial Intelligence

Citations in the corpus

A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, neutral): “Output: Attributes-specific sentence Topic [24, 59, 133, 142], tense [83],politeness [114, 119],”
A survey on fake news and rumour detection techniques (2019, methodology): “Logistic regression was used for stance classification of news articles based on headlines and claims in [35].”
Transfer learning for biomedical named entity recognition with neural networks (2018, methodology): “Additionally, Lample et al. (2016) showed a dropout rate of 0.”
MathPrompter: Mathematical Reasoning using Large Language Models (2023, methodology): “We evaluate MathPrompter on MultiArith dataset Roy & Roth (2016), which is a subset of the Math World Problem Repository Koncel-Kedziorski et al. (2016). This dataset is a collection of”
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples (2017, background): “variety of NLP tasks ranging from machine translation [5, 6] and automatic response generation [20, 21, 22, 23] to semantic constituency parsing [24], and generation of textual descriptions from visual data [25, 26].”
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization (2021, background): “, topics [25], sentence types [36], personas [19], emotions [43, 58], documents [30], or knowledge graphs [17, 27, 54, 59], augmenting the model itself [53, 57], or modifying the loss function [18].”
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities (2022, methodology): “The vocabulary diversity was measured by counting the number of unique bigrams scaled by total number of generated words (distinct-2) [35].”
A Survey of Multilingual Neural Machine Translation (2019, methodology): “In addition, it not uncommon to write domain specific sentences in different styles and so multi-source approaches [168] are not applicable either.”
Recent advances in document summarization (2017, background): “3) Sentence simplification [31,135,165], hierarchical attention [28,135]”
Survey on evaluation methods for dialogue systems (2019, background): “[Li et al., 2016a] propose two loss functions based on maximum mutual information: One is based on an anti-language model, which penalizes high-frequency words, the other is based on the probability of the source given the target.”