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Latest papers without code

Natural Language Generation for Effective Knowledge Distillation

WS 2019

Knowledge distillation can effectively transfer knowledge from BERT, a deep language representation model, to traditional, shallow word embedding-based neural networks, helping them approach or exceed the quality of other heavyweight language representation models.

KNOWLEDGE DISTILLATION LINGUISTIC ACCEPTABILITY SENTENCE SIMILARITY SENTIMENT ANALYSIS TEXT GENERATION TRANSFER LEARNING

StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding

ICLR 2020

Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity and question answering.

LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING PARAPHRASE IDENTIFICATION QUESTION ANSWERING SENTIMENT ANALYSIS

Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments

11 Jan 2019

We use this analysis set to investigate the grammatical knowledge of three pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018), and the BiLSTM baseline from Warstadt et al. We find that these models have a strong command of complex or non-canonical argument structures like ditransitives (Sue gave Dan a book) and passives (The book was read).

CLASSIFICATION LINGUISTIC ACCEPTABILITY

Rating Distributions and Bayesian Inference: Enhancing Cognitive Models of Spatial Language Use

WS 2018

For these models, we propose an extension that simulates a full rating distribution (instead of average ratings) and allows generating individual ratings.

BAYESIAN INFERENCE LINGUISTIC ACCEPTABILITY