Automatically Constructing a Corpus of Sentential Paraphrases

By William B. Dolan, Chris Brockett. 2005

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In Natural Language Processing

Citations in the corpus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019, background): “, 2018) and paraphrasing (Dolan and Brockett, 2005), which aim to predict the relationships between sentences by analyzing them holistically, as well as token-level tasks such as named entity recognition (Tjong Kim Sang and De Meulder, 2003) and SQuAD question answering (Rajpurkar et al.”
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding (2018, methodology): “MRPC The Microsoft Research Paraphrase Corpus (Dolan and Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations of whether the sentences in the pair are semantically equivalent.”
RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019, methodology): “, 2013), Microsoft Research Paragraph Corpus (MRPC) (Dolan and Brockett, 2005), Semantic Textual Similarity Benchmark (STS) (Agirre et al.”
Adversarial Training for Large Neural Language Models (2020, background): “…acceptability (Warstadt et al., 2018), sentiment analysis (Socher et al., 2013), text similarity (Cer et al., 2017), paraphrase detection (Dolan and Brockett, 2005), and natural language inference (NLI) (Da-gan et al., 2006; Bar-Haim et al., 2006; Giampic-colo et al., 2007; Bentivogli et…”
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension (2019, methodology): “Table 2 compares the performance of BART with several recent approaches on the well-studied SQuAD and GLUE tasks (Warstadt et al., 2018; Socher et al., 2013; Dolan & Brockett, 2005; Agirre et al., 2007; Williams et al., 2018; Dagan et al., 2006; Levesque et al., 2011).”
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (2019, background): “Other objectives for sentence embedding learning include predicting future sentences rather than only neighbors (Gan et al., 2017) and predicting explicit discourse markers (Jernite et al., 2017; Nie et al., 2019). Our loss is most similar to the sentence ordering objective of Jernite et al. (2017),”
TinyBERT: Distilling BERT for Natural Language Understanding (2019, background): “MRPC. Microsoft Research Paraphrase Corpus is a paraphrase identification dataset where systems aim to identify if two sentences are paraphrases of each other (Dolan & Brockett, 2005).”
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (2019, methodology): “The Quora Question Pairs, The Microsoft Research Paraphrase Corpus (Dolan and Brockett, 2005), and QNLI, an answer sentence selection task derived from SQuAD (Rajpurkar et al.”
Multi-Task Deep Neural Networks for Natural Language Understanding (2019, background): “Corpus consists of sentence pairs automatically extracted from online news sources with human annotations denoting whether a sentence pair is semantically equivalent to the other in the pair (Dolan and Brockett, 2005).”
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks (2018, methodology): “, 2013); semantic similarity with the MSR Paraphrase Corpus (MRPC; Dolan and Brockett, 2005) and STSBenchmark (STS; Cer et al.”
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding (2019, background): “…of nine NLU tasks as in Table 1, covering textual entailment (RTE [26] and MNLI [27]), question-answer entailment (QNLI [13]), paraphrase (MRPC [28]), question paraphrase (QQP), textual similarity (STS-B [3]), sentiment (SST-2 [5]), linguistic acceptability (CoLA), and Winograd Schema (WNLI…”
SpanBERT: Improving Pre-training by Representing and Predicting Spans (2019, methodology): “UNILM (Dong et al., 2019) uses multiple language modeling objectives – unidirectional (both left-to-right and right-to-left), bidirectional, and sequence-to-sequence prediction – to aid generation tasks like summarization and question generation.”
Unifying Question Answering and Text Classification via Span Extraction (2019, background): “, 2013), MSR Paraphrase Corpus (MRPC) (Dolan and Brockett, 2005), Quora Question Pairs (QQP), Multi-genre Natural Language Inference (MNLI) (Williams et al.”
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (2019, methodology): “, decide whether two sentences are semantically equivalent), we use the Microsoft Research Paraphrase Corpus (MRPC; Dolan and Brockett, 2005).”
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling (2018, methodology): “We use the nine tasks included with GLUE as pretraining and intermediate tasks: acceptability classiﬁcation with CoLA (Warstadt et al., 2018); binary sentiment classiﬁcation with SST (Socher et al., 2013); semantic similarity with the MSR Paraphrase Corpus ( MRPC ; Dolan and Brockett, 2005), Quora Q”
Finetuned Language Models Are Zero-Shot Learners (2021, neutral): “MRPC (Dolan & Brockett, 2005) 2.”