Papers with LSTM structure
Sentence-State LSTM for Text Representation (P18-1)
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| Challenge: | LSTMs have been shown to suffer from various limitations due to their sequential nature. |
| Approach: | They propose to model hidden states of all words simultaneously at each recurrent step rather than one word at a time. |
| Outcome: | The proposed model has strong representation power, giving competitive performances compared to stacked BiLSTM models with similar parameter numbers. |
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph. |
| Approach: | They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure. |
| Outcome: | The proposed model outperforms existing methods on a benchmark. |