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natural-language-processing-10

Natural Language Understanding

3260 papers • 126 benchmarks • 313 datasets

Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension. Applications enabled by natural language understanding range from question answering to automated reasoning. Source: Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

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Benchmarks

These leaderboards are used to track progress in natural-language-understanding-10

Trend

Dataset

Best Model

Actions

PDP60

PDP60

STREUSLE

STREUSLE

LexGLUE

LexGLUE

Libraries

i

Use these libraries to find natural-language-understanding-10 models and implementations

huggingface/transformers

11 papers 124,889

Datasets

GLUE

MMLU

WinoGrande

MultiWOZ

SGD

CLUE

Subtasks

Vietnamese Social Media Text Processing Emotional Dialogue Acts

Most implemented papers

Attention is All you Need

Noam Shazeer, Ashish Vaswani, Lukasz Kaiser, Jakob Uszkoreit, Niki Parmar, I. Polosukhin, Llion Jones, Aidan N. Gomez•Sun Jun 11 2017

A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

164803

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

GLUE

GLUE

DialoGLUE full

DialoGLUE full

DialoGLUE fewshot

DialoGLUE fewshot

7 papers 2,199

alibaba/EasyNLP

6 papers 1,946

microsoft/MT-DNN

4 papers 153

faceonlive/ai-research

4 papers 144

microsoft/unilm

3 papers 18,315

labmlai/annotated_deep_learning_pap…

2 papers 47,992

2 papers 2,548

moein-shariatnia/OpenAI-CLIP

2 papers 513

facebookresearch/data2vec_vision

2 papers 75

WritingPrompts

CosmosQA

LogiQA

AQUA-RAT

0

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018

A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

109344 0

Learning Transferable Visual Models From Natural Language Supervision

I. Sutskever, Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger•Thu Feb 25 2021

It is demonstrated that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet.

39743 0

A Neural Conversational Model

Quoc V. Le, O. Vinyals•Thu Jun 18 2015

A simple approach to conversational modeling which uses the recently proposed sequence to sequence framework, and is able to extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles.

1799 0

Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

A. Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, A. Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, F. Caltagirone, Thibaut Lavril, Maël Primet, J. Dureau•Thu May 24 2018

The machine learning architecture of the Snips Voice Platform is presented, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices that is fast and accurate while enforcing privacy by design, as no personal user data is ever collected.

888 0

Cross-lingual Language Model Pretraining

Alexis Conneau, Guillaume Lample•Mon Jan 21 2019

This work proposes two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingsual language model objective.

2915 0

BERT for Joint Intent Classification and Slot Filling

Qian Chen, Zhu Zhuo, Wen Wang•Wed Feb 27 2019

This work proposes a joint intent classification and slot filling model based on BERT that achieves significant improvement on intent classification accuracy, slot filling F1, and sentence-level semantic frame accuracy on several public benchmark datasets, compared to the attention-based recurrent neural network models and slot-gated models.

598 0

Neural Architecture Search with Reinforcement Learning

Quoc V. Le, Barret Zoph•Thu Nov 03 2016

This paper uses a recurrent network to generate the model descriptions of neural networks and trains this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.

5748 0

Adding a benchmark result helps the community track progress.

Natural Language Understanding | State-of-the-Art