Papers with building sentence representations
Parameter-free Sentence Embedding via Orthogonal Basis (D19-1)
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| Challenge: | Existing methods to build sentence embeddings are parameterized and require training to optimize their parameters. |
| Approach: | They propose a non-parameterized method to combine pre-trained word embeddings into sentence representations using an orthogonal basis of the word vector subspace and its surrounding context. |
| Outcome: | The proposed method shows superior performance on 11 downstream NLP tasks and is competitive to other methods relying on large amounts of labelled data or prolonged training time. |
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis (D19-1)
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| Challenge: | Existing deep neural network models such as LSTM and tree-LSTM have a bias problem where the words in the tail of a sentence are more heavily emphasized than those in the header. |
| Approach: | They propose a capsule tree-LSTM model that uses dynamic routing to build sentence representations by assigning different weights to nodes according to their contributions to prediction. |
| Outcome: | The proposed model improves on the Stanford Sentiment Treebank and EmoBank datasets. |