Papers with DiscoEval
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations (D19-1)
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| Challenge: | Prior work on pretrained sentence embeddings and benchmarks focused on the capabilities of stand-alone sentences. |
| Approach: | They propose a test suite of tasks to evaluate whether sentence representations include broader context information. |
| Outcome: | The proposed training objectives help to encode different aspects of information in document structures. |
SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020.emnlp-main)
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| Challenge: | Recent models for learning discourse language representations focus on bottom or top-level representations, but they do not capture intermediate-size structures in natural languages such as sentences and the relationships among them. |
| Approach: | They propose a new objective for learning a discourse language representation in a self-supervised manner by shuffling the sequence of input sentences and training a hierarchical transformer model to reconstruct the original ordering. |
| Outcome: | The proposed model improves the original BERT model on downstream tasks by large margins. |
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. |