Learning Discourse-Aware Sentence Representations from Document Structures

Author: Mingda Chen. Table of Links Abstract Acknowledgements 1 INTRODUCTION 1.1 Overview 1.2 Contributions 2 BACKGROUND 2.1 Self-Supervised Language Pretraining 2.2 Naturally-Occurring Data Structures 2.3 Sentence Variational Autoencoder 2.4 Summary 3 IMPROVING SELF-SUPERVISION FOR LANGUAGE PRETRAINING 3.1 Improving Language Representation Learning via Sentence Ordering Prediction 3.2 Improving In-Context Few-Shot Learning via Self-Supervised Training 3.

, we use the averaged vector at the position of the token across all layers. We also evaluate per-layer performance for both models in Section 4.2.5. When reporting results for SentEval, we compute the averaged Pearson correlations for Semantic Textual Similarity tasks from 2012 to 2016 . We refer to the average as unsupervised semantic similarity since those tasks do not require training data.

-base, we use the vector from the position of the token. Fig. 4.4 shows the heatmap of performance for individual hidden layers. We note that for better visualization, colors in each column are standardized. On SentEval, -large by a large margin, we observe that within some specific domains, for example Wiki in BSO,

and Skip-thought vectors, which have training losses explicitly related to surrounding sentences, perform much stronger compared to their respective prior work, demonstrating the effectiveness of incorporating losses that make use of broader context. 4.2.2 Related Work Discourse modelling and discourse parsing have a rich history , much of it based on recovering linguistic annotations of discourse structure.

to ELMo and Skip-thought to InferSent on DiscoEval, we can see the benefit of adding information about neighboring sentences. Our proposed training objectives show complementary improvements over NSP, which suggests that they can potentially benefit these pretrained representations. 4.2.5 Analysis Per-Layer analysis . To investigate the performance of individual hidden layers, we evaluate ELMo and

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