StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure (2023.emnlp-main)
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| Challenge: | Structured Autoencoder framework StrAE enables effective learning of multi-level representations through strict adherence to explicit structure. |
| Approach: | They propose a Structured Autoencoder framework that strictly adheres to explicit structure and uses a contrastive objective over tree-structured representations. |
| Outcome: | The proposed framework outperforms baselines that don’t involve explicit hierarchical compositions and is comparable to models given informative structure. |
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
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How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases (2023.acl-long)
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| Challenge: | a recent study found that pre-training can teach language models to rely on hierarchical syntactic features . aaron ramirez: we find that pretraining on simpler language induces a hierarchic bias . |
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Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | Existing work on tree structures uses syntactic parsers or Treebank annotations to perform target-dependent classifications. |
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StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (P18-1)
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| Challenge: | Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. |
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Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs (2020.acl-main)
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| Challenge: | a constituency-based network generalizes more robustly than a dependency-based one . a sequential LSTM can learn the appropriate rules governing these dependencies . |
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Autoencoding Improves Pre-trained Word Embeddings (2020.coling-main)
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| Challenge: | Existing work has shown that word embeddings are distributed in a narrow cone and that centering and projection can improve the accuracy of pre-trained word embeds without requiring additional training data. |
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LV-BERT: Exploiting Layer Variety for BERT (2021.findings-acl)
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| Challenge: | Modern pre-trained language models are mostly built upon stereotyped development sets . LV-BERT model obtained by our method outperforms BERT on various downstream tasks . |
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Learned Incremental Representations for Parsing (2022.acl-long)
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| Challenge: | a new syntactic representation that commits to syntakic choices is proposed for humans . we use a system that uses only incremental processing of a prefix to predict the word in a sentence . |
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Constituency Parsing with a Self-Attentive Encoder (P18-1)
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| Challenge: | Recent work on LSTM encoders based on recurrent neural networks has led to improvements in constituency parsing accuracy. |
| Approach: | They propose to replace an LSTM encoder with a self-attentive architecture to improve a discriminative constituency parser. |
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HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings (2023.findings-emnlp)
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| Challenge: | Existing methods that encode a sequence in its entirety for contrast with others often neglect local representation learning. |
| Approach: | They propose a hierarchical contrastive learning framework, HiCL, which considers local segment-level and global sequence-level relationships to improve training efficiency and effectiveness. |
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