Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

By Ankur P. Parikh, Shay B. Cohen, Eric P. Xing. 2014

551 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, methodology): “[124] explore the task of few-shot semantic parsing using LMs by (1) framing the semantic parsing task as a paraphrasing task [7] and (2) constraining the decoding process by only allowing output valid according to a grammar.”
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks (2016, background): “Word-level distributed representations have been analyzed rather extensively, both empirically and theoretically, for example by Baroni et al. (2014), Levy & Goldberg (2014) and Levy et al. (2015).”
Top2Vec: Distributed Representations of Topics (2020, unknown): “The word2vec model generated word vectors produced state-of-the-art results on many linguistics tasks compared to traditional methods [8, 9, 10, 11].”
Language Models can Solve Computer Tasks (2023, background): “1 Automated computer tasks The automation of computer tasks is an important topic for both information retrieval and natural language processing [32, 39, 38, 24, 50].”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, background): “For instance, different methods for obtaining vector representations of words, also known as word embeddings, have been proposed, and their quality may be evaluated on a variety of tasks [22].”
Recent advances in document summarization (2017, background): “This idea has been studied as a query-chain summarization task [10],”
Bootstrapping language acquisition. (2017, background): “…and has received considerable attention recently in the computational literature on semantic parser induction (e.g., Artzi, Das, & Petrov, 2014; Krishnamurthy & Mitchell, 2014; Kwiatkowski et al., 2010, 2011; Matuszek, Fitzgerald, Zettlemoyer, Bo, & Fox, 2012; Zettlemoyer & Collins, 2005,…”
A Survey on Application of Knowledge Graph (2020, background): “For example, in [14], linguistic information such as”
Factor-based Compositional Embedding Models (2014, background): “Previous work on compositional phrase semantics [14, 15] can be seen as special cases of this type of models for phrases.”
Transfer Learning for User Adaptation in Spoken Dialogue Systems (2016, background): “Negotiation dialogue has been lately solved by abandoning the single-agent reinforcement learning paradigm in favour of multi-agent reinforcement learning [18] and stochastic games [2].”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “This is highly useful when a document has a wide range and mix of sentiments associated with it (Yang and Cardie 2014).”
Neural information retrieval: at the end of the early years (2017, background): “In contrast to the conclusions obtained in Baroni et al. (2014), the success of context-predicting models is attributed to choice of hyper-parameters, which can also be used for context-counting”