Papers with neural sequence-to-sequence
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages (N18-1)
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| Challenge: | Morphological segmentation for polysynthetic languages is challenging because of limited training data. |
| Approach: | They propose two new multi-task training approaches that improve performance for Mexican polysynthetic languages . they also propose cross-lingual transfer as a third way to fortify their neural model . |
| Outcome: | The proposed models improve on Mexicanero, Nahuatl, Wixarika and Yorem Nokki . the proposed models reduce the amount of parameters by close to 75% . |
Fluency Boost Learning and Inference for Neural Grammatical Error Correction (P18-1)
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| Challenge: | Seq2seq models for grammatical error correction (GEC) have two limitations: (1) a seq2q model may not be well generalized with only limited error-corrected data; (2) a model may fail to completely correct a sentence with multiple errors through normal seq1sequeq inference. |
| Approach: | They propose a fluency boost learning and inference mechanism to improve the performance of seq2seq models for grammatical error correction (GEC) by generating fluency-boost sentence pairs during training. |
| Outcome: | Experiments show that the proposed model improves on both CoNLL-2014 and JFLEG benchmark datasets. |
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning (2020.coling-main)
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| Challenge: | Paraphrase generation is of great importance for many downstream tasks in natural language processing. |
| Approach: | They propose a method to generate sentences as learning objectives from the learned data distribution and employ reinforcement learning to combine these new learning objectives for model training. |
| Outcome: | The proposed method gains significant diversity and improves generation quality over state-of-the-art datasets. |
COD3S: Diverse Generation with Discrete Semantic Signatures (2020.emnlp-main)
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| Challenge: | Existing methods for generating semantically diverse sentences are based on locality-sensitive hash (LSH)-based semantic sentence codes that explicitly capture meaningful semantic differences. |
| Approach: | They propose a method for generating semantically diverse sentences using neural sequence-to-sequence models by conditioned on locality-sensitive hash-based semantic sentence codes whose Hamming distances correlate with human judgments of semantic textual similarity. |
| Outcome: | The proposed method improves output diversity without degrading performance on causal generation tasks. |