Quora Question Pairs

By Zihang Chen, Hongbo Zhang, Xiaoji Zhang, Le Zhao. 2017

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

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

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019, background): “QQP Quora Question Pairs is a binary classification task where the goal is to determine if two questions asked on Quora are semantically equivalent (Chen et al., 2018).”
TinyBERT: Distilling BERT for Natural Language Understanding (2019, background): “The task is to determine whether two questions are semantically equivalent (Chen et al., 2018).”
Multi-Task Deep Neural Networks for Natural Language Understanding (2019, background): “The task is to predict whether two questions are semantically equivalent (Chen et al., 2018).”
RoFormer: Enhanced Transformer with Rotary Position Embedding (2021, methodology): “We look at several datasets from GLUE, i.e. MRPC Dolan and Brockett [2005], SST-2 Socher et al. [2013], QNLI Rajpurkar et al. [2016], STS-B Al-Natsheh [2017], QQP Chen et al. [2018b] and MNLI Williams et al. [2018].”
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers (2020, background): “GLUE The General Language Understanding Evaluation (GLUE) benchmark (Wang et al., 2019) consists of nine sentence-level classiﬁcation tasks, including Corpus of Linguistic Acceptability (CoLA) (Warstadt et al., 2018), Stanford Sentiment Treebank (SST) (Socher et al., 2013), Microsoft Research Paraph”
Linformer: Self-Attention with Linear Complexity (2020, methodology): “…our Linformer on IMDB (Maas et al., 2011) and SST-2 (Socher et al., 2013) (sentiment classiﬁcation), as well as QNLI (natural language inference) (Rajpurkar et al., 2016), and QQP (textual similarity) (Chen et al., 2018) We do the same with RoBERTa, 12-layer BERT-base and 6-layer distilled BERT.”
Well-Read Students Learn Better: The Impact of Student Initialization on Knowledge Distillation (2019, methodology): “Since strictly in-domain data is difﬁcult to obtain, we supplement D T with two other sentence-pair datasets: SNLI (Bowman et al., 2015) and QQP (Chen et al., 2018).”
Nyströmformer: A Nystöm-based Algorithm for Approximating Self-Attention (2021, background): “2016), QQP (Chen et al. 2018), and MNLI (Williams, Nangia, and Bowman 2018) in GLUE benchmark and IMDB reviews (Maas et al.”
Luna: Linear Unified Nested Attention (2021, background): “…natural language processing tasks, including sentiment classiﬁcation (SST-2; Socher et al., 2013), natural language inference (QNLI; Rajpurkar et al., 2016), textual similarity (QQP; Chen et al., 2018, question answering (RACE (Lai et al., 2017) and CommonsenseQA (CSQA; Talmor et al., 2019).”
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search (2020, methodology): “MRPC (Dolan and Brockett, 2005) and QQP (Chen et al., 2018) are adopted for semantic equivalence classiﬁcation, whose sentence pairs are extracted from news sources and online question-answering website respectively.”
Block Pruning For Faster Transformers (2021, background): “1 Ra-jpurkar et al., 2016) and (SQuAD v2 Rajpurkar et al., 2018), natural language inference (MNLI Williams et al., 2018), sentence similarity (QQP Chen et al., 2018), sentiment classiﬁcation (SST-2 Socher et al., 2013) and abstractive summarization (CNN/DailyMail Hermann et al., 2015).”
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View (2019, methodology): “QQP The Quora Question Pairs 8 [9] dataset is a collection of question pairs from the community question-answering website Quora.”
Language model compression with weighted low-rank factorization (2022, methodology): “Lastly, we add BERT-PKD (Sun et al., 2019) based on its reproduced results (Chen et al., 2018a) for comparison.”
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering (2020, background): “QQP (Quora Question Pairs) (Iyer et al., 2019) consists of over 400,000 potential duplicate question pairs from Quora.”
Larger language models do in-context learning differently (2023, unknown): “…Sentence Classification (Conneau & Kiela, 2018, SUBJ); Question Classification (Li & Roth, 2002, TREC); Duplicated-Question Recognition (Chen et al., 2017; Wang et al., 2018, QQP); Textual Entailment Recognition (Dagan et al., 2006; Wang et al., 2019, RTE); Financial Sentiment Analysis…”
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding (2020, background): “Specifically, For QQP and MRPC, the metric is F1-score, for STS-B, the metric is Matthew’s correlation, the rest tasks use accuracy.”
Decoupled Transformer for Scalable Inference in Open-domain Question Answering (2021, background): “We observe that tasks with a large dataset (QQP, SQUAD, MNLI contain over 100K samples each) have similar behavior with a noticeable drop when moving from decoupled transformer 5-7 split to 6-6 split, and another big drop when the number of cross-component layers becomes less than 3.”