all-MiniLM-L6-v2

Papers using or citing this model 11

• A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference 5.3K citations · 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · DOI

  This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. At 433k examples, this resource is...

• A large annotated corpus for learning natural language inference 4.9K citations · 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · DOI

  Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine...

• SPECTER: Document-level Representation Learning using Citation-informed Transformers 953 citations · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · DOI

  Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and...

• ELI5: Long Form Question Answering 881 citations · 2019 · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · DOI

  We introduce the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum “Explain Like I’m Five” (ELI5)...

• SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine 531 citations · 2017 · arXiv (Cornell University) · arXiv

  We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to...

• Open question answering over curated and extracted knowledge bases 451 citations · 2014 · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · DOI

  We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this...

• WikiHow: A Large Scale Text Summarization Dataset 356 citations · 2018 · arXiv (Cornell University) · arXiv

  Sequence-to-sequence models have recently gained the state of the art performance in summarization. However, not too many large-scale high-quality datasets are available and almost all the available ones are mainly news articles with...

• PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them 286 citations · 2021 · arXiv

  Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of speed and memory compared to conventional models which...

• A Repository of Conversational Datasets 97 citations · 2019 · Proceedings of the Workshop on NLP for Conversational AI (2019) · arXiv

  Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of...

• MS MARCO: Benchmarking Ranking Models in the Large-Data Regime 87 citations · 2021 · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · DOI

  Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field. However, the goal is not simply to...

• GooAQ: Open Question Answering with Diverse Answer Types 77 citations · 2021 · arXiv

  While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with...