Natural Language Understanding
660 papers with code • 11 benchmarks • 71 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
GLUE
MMLU
WinoGrande
MultiWOZ
SGD
CosmosQA
LogiQA
Most implemented papers
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.
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
The pre-trained language models have achieved great successes in various natural language understanding (NLU) tasks due to its capacity to capture the deep contextualized information in text by pre-training on large-scale corpora.
Unified Language Model Pre-training for Natural Language Understanding and Generation
microsoft/unilm
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NeurIPS 2019
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
microsoft/DeBERTa
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ICLR 2021
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
pytorch/fairseq
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Preprint 2022
While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind.
Benchmarking Natural Language Understanding Services for building Conversational Agents
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
stanford-futuredata/ColBERT
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ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
Multi-Task Deep Neural Networks for Natural Language Understanding
In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks.
