Papers with composition function
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations (2022.naacl-srw)
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| Challenge: | Existing tree-based models require handannotated data to be trained. |
| Approach: | They propose a tree-based model that learns its composition function together with its structure. |
| Outcome: | The proposed model outperforms existing models on downstream tasks and is competitive with Bert base model. |
Neural Compositional Denotational Semantics for Question Answering (D18-1)
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| Challenge: | a new model for compositional questions is needed to answer multi-step reasoning . the model is inspired by formal approaches to compositional semantics . |
| Approach: | They propose an end-to-end differentiable model for interpreting compositional questions . they build a latent tree of interpretable expressions over a sentence . |
| Outcome: | The proposed model outperforms RNN encoders when test questions are longer than training questions. |
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification (2020.coling-main)
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| Challenge: | Existing methods for learning multi-word expressions have language sparsity and are not supervised. |
| Approach: | They propose an unsupervised approach to learning a compositional representation function for multi-word expressions . they use a Tratz dataset to train the composition function on the word-semantic relation . |
| Outcome: | The proposed method outperforms the previous state-of-the-art method on the Tratz dataset with an F1 score of 50.4%. |
Learning from Non-Binary Constituency Trees via Tensor Decomposition (2020.coling-main)
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| Challenge: | a binarisation procedure changes the structure of constituency trees, furthering constituents that are not binary. |
| Approach: | They propose a binarised approach to binarise constituency trees by tensor-based models . they propose 'trunk-LSTM' model which exploits such a rich structure . |
| Outcome: | The proposed model performs well on different NLP tasks. |
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)
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| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
| Approach: | They propose to model teacher-learner dynamics through natural interactions occurring between users and search engines. |
| Outcome: | The proposed model is better than non-grounded models on compositionality and zero-shot inference tasks. |