Neural Attention for Learning to Rank Questions in Community Question Answering

@inproceedings{Romeo2016NeuralAF,
  title={Neural Attention for Learning to Rank Questions in Community Question Answering},
  author={Salvatore Romeo and Giovanni Da San Martino and Alberto Barr{\'o}n-Cede{\~n}o and Alessandro Moschitti and Yonatan Belinkov and Wei-Ning Hsu and Shuyuan Zhang and Mitra Mohtarami and James R. Glass},
  booktitle={COLING},
  year={2016}
}
In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 12 CITATIONS

References

Publications referenced by this paper.
SHOWING 1-10 OF 39 REFERENCES

Similar Papers

Loading similar papers…