Papers by Larry Lansing

1 papers
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models (2020.acl-main)

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Challenge: Recent models for unsupervised representation learning of text have put little focus on discourse-level representations.
Approach: They propose an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences.
Outcome: The proposed model outperforms the BERT-Large model on the discourse representation benchmark DiscoEval and yields gains of 2%-6% absolute even for tasks that do not explicitly evaluate discourse.

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