Natural Language Understanding

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?

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1651–1660 of 1978 papers

Title	Date	Tasks	Status
Hybrid MemNet for Extractive Summarization	Dec 25, 2019	Document SummarizationExtractive Summarization	—Unverified
Learning and Evaluating Contextual Embedding of Source Code	Dec 21, 2019	Contextual Embedding for Source CodeException type	CodeCode Available
WaLDORf: Wasteless Language-model Distillation On Reading-comprehension	Dec 13, 2019	Language ModelingLanguage Modelling	—Unverified
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators	Dec 2, 2019	Dialogue Act ClassificationEmotional Dialogue Acts	CodeCode Available
Argument Mining: A Survey	Dec 1, 2019	Argument MiningNatural Language Understanding	—Unverified
Semi-supervised Visual Feature Integration for Pre-trained Language Models	Dec 1, 2019	Natural Language InferenceNatural Language Understanding	—Unverified
Designing the Next Generation of Intelligent Personal Robotic Assistants for the Physically Impaired	Nov 28, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Cost-Based Goal Recognition Meets Deep Learning	Nov 22, 2019	Deep LearningIntent Detection	—Unverified
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems	Nov 21, 2019	Dialogue State TrackingIntent Detection	CodeCode Available
Learning to Control Latent Representations for Few-Shot Learning of Named Entities	Nov 19, 2019	Few-Shot LearningManagement	—Unverified

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All datasets PDP60 STREUSLE LexGLUE DialoGLUE fewshot DialoGLUE full GLUE

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	HNN	Accuracy	90	—	Unverified
2	UDSSM-II (ensemble)	Accuracy	78.3	—	Unverified
3	BERT-large 340M	Accuracy	78.3	—	Unverified
4	UDSSM-I (ensemble)	Accuracy	76.7	—	Unverified
5	DSSM	Accuracy	75	—	Unverified
6	UDSSM-II	Accuracy	75	—	Unverified
7	BERT-base 110M + MAS	Accuracy	68.3	—	Unverified
8	USSM + Supervised Deepnet + 3 Knowledge Bases	Accuracy	66.7	—	Unverified
9	Word-level CNN+LSTM (full scoring)	Accuracy	60	—	Unverified
10	Subword-level Transformer LM	Accuracy	58.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT (pred POS/lemmas)	Tags (Full) Acc	82.5	—	Unverified
2	BERT (none)	Tags (Full) Acc	82	—	Unverified
3	BERT (gold POS/lemmas)	Tags (Full) Acc	81	—	Unverified
4	GloVe (gold POS/lemmas)	Tags (Full) Acc	79.3	—	Unverified
5	RoBERTa + Linear	Full F1 (Preps)	78.2	—	Unverified
6	GloVe (none)	Tags (Full) Acc	77.5	—	Unverified
7	GloVe (pred POS/lemmas)	Tags (Full) Acc	77.1	—	Unverified
8	SVM (feature-rich, gold syntax)	Role F1 (Preps)	62.2	—	Unverified
9	BiLSTM + MLP (gold syntax)	Role F1 (Preps)	62.2	—	Unverified
10	SVM (feature-rich, auto syntax)	Role F1 (Preps)	58.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CaseLaw-BERT	CaseHOLD	75.6	—	Unverified
2	Legal-BERT	CaseHOLD	75.1	—	Unverified
3	DeBERTa	CaseHOLD	72.1	—	Unverified
4	Longformer	CaseHOLD	72	—	Unverified
5	RoBERTa	CaseHOLD	71.7	—	Unverified
6	BERT	CaseHOLD	70.7	—	Unverified
7	BigBird	CaseHOLD	70.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ConvBERT-DG	Average	74.6	—	Unverified
2	ConvBERT-DG + Pre + Multi	Average	73.8	—	Unverified
3	mslm	Average	73.49	—	Unverified
4	ConvBERT + Pre + Multi	Average	68.22	—	Unverified
5	BanLanGen	Average	39.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ConvBERT + Pre + Multi	Average	86.89	—	Unverified
2	mslm	Average	85.83	—	Unverified
3	ConvBERT-DG + Pre + Multi	Average	85.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MT-DNN-SMART	Average	89.9	—	Unverified
2	BERT-LARGE	Average	82.1	—	Unverified