Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

By Regina Barzilay, Min-Yen Kan. 2017

245 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (2021, background): “• Data Augmentation Data augmentation is a technique that targets increasing the amount of data that can be used for training by making modiications to existing data [26, 109].”
Survey of Hallucination in Natural Language Generation (2022, neutral): “The source in abstractive summarization is the input source text that is being summarized [165], while the source in data-totext is non-linguistic data [56, 157], and the source(s) in the dialogue system is dialogue history and/or the external knowledge sentences.”
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers (2020, neutral): “For the CNN/DailyMail dataset, the total length is 768 and the maximum output length is 160 .”
Consistent Accelerated Inference via Confident Adaptive Transformers (2021, methodology): “Softmax values are calibrated using temperature scaling (Guo, 2017) on another held-out (labeled) data split, Dscale.”
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow (2018, neutral): “For a particular class of applications, such as source code retrieval given a NL query [42], source code summarization in NL [15], and source code synthesis from NL [33, 47], all of which involve correspondence between NL utterances and code, it is essential to have access to high volume, high quali”
BARTScore: Evaluating Generated Text as Text Generation (2021, neutral): “Abstractive Systems A1: Ptr-Gen [62]; A2: Bottom-up [17]; A3: Fast-Abs-RL [5]; A4: Two-stage-RL [74]; A5: BERT-Ext-Abs [41]; A6: BERT-Abs [41]; A7: Trans-Abs [41]; A8: UniLM-1 [10]; A9: UniLM-2 [2]; A10: T5-base [56]; A11: T5-large [56]; A12: T5-11B [56]; A13: BART [33]; A14: SemSim [42].”
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities (2022, neutral): “How users adapt to systems over time is of interest for not only HCI researchers but also NLP researchers [59, 60].”
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020, unknown): “Some work optimizes the retrieval module to aid in a specific, downstream task such as question answering, using search [46], reinforcement learning [6, 63, 62], or a latent variable approach [31, 20] as in our work.”
Autoregressive Entity Retrieval (2020, unknown): “…chat-bots and question answering systems, that are often equipped with retrieval components to surface speciﬁc KB entries (e.g., Wikipedia articles) to ﬁnd knowledge for sustaining a conversation or answering a question (Ferrucci, 2012; Chen et al., 2017; Lewis et al., 2020; Roller et al., 2020).”
Federated Adversarial Debiasing for Fair and Transferable Representations (2021, unknown): “Adversarial Learning has beenwidely applied in various domains, such as neural language recognition [27], image-to-image (dense) prediction [31], image generation [12], and etc.”
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis (2020, unknown): “proposed one of the first models, bc-LSTM, which utilized this design along with bi-directional LSTMs for the inter-utterance representation learning, framing the overall problem as a structured prediction (sequence tagging) task [44].”
Natural language processing: state of the art, current trends and challenges (2017, unknown): “[154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree.”
A Survey of Multilingual Neural Machine Translation (2019, methodology): “Multi-source NMT has also been used for system combination, which combines NMT and SMT outputs to improve translation performance [166].”
A survey on fake news and rumour detection techniques (2019, methodology): “Similarly, LIAR [101] contains short political statements, obtained through the website PolitiFact.com12.”