Papers with neural encoder-decoder models
Cross-Sentence Grammatical Error Correction (P19-1)
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| Challenge: | Existing approaches to automatic grammatical error correction (GEC) ignore cross-sentence context . existing approaches only correct one sentence at a time and ignore useful contextual information . |
| Approach: | They propose to use an auxiliary encoder that encodes previous sentences and incorporates the encoding in the decoder via attention and gating mechanisms. |
| Outcome: | The proposed model improves over strong baselines on a synthetic dataset showing high performance in verb tense corrections that require cross-sentence context. |
Event2Mind: Commonsense Inference on Events, Intents, and Reactions (P18-1)
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| Challenge: | Using a crowdsourced corpus of 25,000 event phrases, we construct a new task that uses commonsense reasoning to reason about the likely intents and reactions of the event participants. |
| Approach: | They construct a crowdsourced corpus of 25,000 event phrases and use them to construct 'commonsense inference' they demonstrate that neural encoder-decoder models can compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. |
| Outcome: | The proposed task can be used to uncover implicit gender inequality in movie scripts. |
Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
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| Challenge: | Recent neural network models conflate content selection and surface realization into a black-box architecture, resulting in content to be described in text cannot be explicitly controlled. |
| Approach: | They propose to decouple content selection from the decoder to allow finer-grained control over the generation. |
| Outcome: | The proposed model can be trained end-to-end without human annotations and achieves promising results in data-totext and headline generation tasks. |
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation (C18-1)
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| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
| Approach: | They propose an easy-to-extend learning framework based on latent vectors to provide training guidance without resorting to extra data or complicating network’s inner structure. |
| Outcome: | The proposed framework improves the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies. |
Hierarchy Response Learning for Neural Conversation Generation (D19-1)
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| Challenge: | Neural conversation generation models can't perceive and express the intention effectively, causing dull and generic responses. |
| Approach: | They propose a hierarchical response generation model to capture conversation intention . they propose an expression reconstruction model and an expression attention model . |
| Outcome: | The proposed model can generate the responses with more appropriate content and expression. |