Abstract
Embedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words with only dozens of pre-defined relations, mostly morphological relations and named entities. In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. We present CA-EHN, the first commonsense word analogy dataset containing 90,505 analogies covering 5,656 words and 763 relations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense knowledge. The dataset is publicly available at https://github.com/ckiplab/CA-EHN.- Anthology ID:
- 2020.lrec-1.365
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 2984–2990
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.365
- DOI:
- Cite (ACL):
- Peng-Hsuan Li, Tsan-Yu Yang, and Wei-Yun Ma. 2020. CA-EHN: Commonsense Analogy from E-HowNet. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2984–2990, Marseille, France. European Language Resources Association.
- Cite (Informal):
- CA-EHN: Commonsense Analogy from E-HowNet (Li et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.lrec-1.365.pdf
- Code
- ckiplab/CA-EHN