Papers with Tree Kernels
Injecting Relational Structural Representation in Neural Networks for Question Similarity (P18-2)
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| Challenge: | Recent years have seen exponential growth and use of web forums, where users can exchange and find information just asking questions in natural language. |
| Approach: | They propose to use Tree Kernels to learn a model on relatively few pairs of questions as gold standard (GS) predicting labels on a very large corpus of question pairs is also a useful approach, they propose . |
| Outcome: | The proposed model can learn more accurate models after fine tuning on GS. |
Correlating Neural and Symbolic Representations of Language (P19-1)
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| Challenge: | a popular technique for analyzing neural representations involves predicting information of interest from the activation patterns. |
| Approach: | They propose to use Representational Similarity Analysis and Tree Kernels to quantify how strongly activation patterns correspond to symbolic representations. |
| Outcome: | The proposed methods show that they exhibit the expected pattern of results on a synthetic language. |
Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics (2022.coling-1)
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| Challenge: | Sentence Textual Similarity (STS) is a classic task that can be applied to downstream NLP applications such as text generation and retrieval. |
| Approach: | They propose a light-weighted Expectation-Correction (EC) formulation for STS computation. |
| Outcome: | The proposed approach is more efficient and scalable than previous approaches. |