Natural Language Understanding
594 papers with code • 9 benchmarks • 67 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?
Benchmarks
These leaderboards are used to track progress in Natural Language Understanding
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Libraries
Use these libraries to find Natural Language Understanding models and implementations
MultiWOZ
MMLU
WinoGrande
SGD
CosmosQA
LogiQA
LexGLUE
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
A Neural Conversational Model
We find that this straightforward model can generate simple conversations given a large conversational training dataset.
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
google-research/electra
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ICLR 2020
Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
Cross-lingual Language Model Pretraining
huggingface/transformers
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NeurIPS 2019
On unsupervised machine translation, we obtain 34. 3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU.
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
Know What You Don't Know: Unanswerable Questions for SQuAD
Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
ofa-sys/ofa
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WS 2018
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset.
Improving Language Understanding by Generative Pre-Training
huggingface/transformers
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Preprint 2018
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
