Abstract
We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums. The model links different context with related language factors in the embedding space, providing a way to interpret the factored embeddings. Evaluated on a community endorsement prediction task using a large collection of topic-varying Reddit discussions, the factored embeddings consistently achieve improvement over other text representations. Qualitative analysis shows that the model captures community style and topic, as well as response trigger patterns.- Anthology ID:
- D17-1243
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2296–2306
- Language:
- URL:
- https://aclanthology.org/D17-1243
- DOI:
- 10.18653/v1/D17-1243
- Cite (ACL):
- Hao Cheng, Hao Fang, and Mari Ostendorf. 2017. A Factored Neural Network Model for Characterizing Online Discussions in Vector Space. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2296–2306, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- A Factored Neural Network Model for Characterizing Online Discussions in Vector Space (Cheng et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D17-1243.pdf
- Code
- hao-cheng/factored_neural