12 research outputs found
Learning Language Representations for Typology Prediction
One central mystery of neural NLP is what neural models "know" about their
subject matter. When a neural machine translation system learns to translate
from one language to another, does it learn the syntax or semantics of the
languages? Can this knowledge be extracted from the system to fill holes in
human scientific knowledge? Existing typological databases contain relatively
full feature specifications for only a few hundred languages. Exploiting the
existence of parallel texts in more than a thousand languages, we build a
massive many-to-one neural machine translation (NMT) system from 1017 languages
into English, and use this to predict information missing from typological
databases. Experiments show that the proposed method is able to infer not only
syntactic, but also phonological and phonetic inventory features, and improves
over a baseline that has access to information about the languages' geographic
and phylogenetic neighbors.Comment: EMNLP 201
Bayesian Agglomerative Clustering with Coalescents
We introduce a new Bayesian model for hierarchical clustering based on a
prior over trees called Kingman's coalescent. We develop novel greedy and
sequential Monte Carlo inferences which operate in a bottom-up agglomerative
fashion. We show experimentally the superiority of our algorithms over others,
and demonstrate our approach in document clustering and phylolinguistics.Comment: NIPS 200
Uncovering Probabilistic Implications in Typological Knowledge Bases
The study of linguistic typology is rooted in the implications we find
between linguistic features, such as the fact that languages with object-verb
word ordering tend to have post-positions. Uncovering such implications
typically amounts to time-consuming manual processing by trained and
experienced linguists, which potentially leaves key linguistic universals
unexplored. In this paper, we present a computational model which successfully
identifies known universals, including Greenberg universals, but also uncovers
new ones, worthy of further linguistic investigation. Our approach outperforms
baselines previously used for this problem, as well as a strong baseline from
knowledge base population.Comment: To appear in Proceedings of ACL 201
言語変化と系統への統計的アプローチ
要旨あり統計的言語研究の現在研究詳