Daniel Gildea

Exploring phrase-compositionality in skip-gram models

2016 · arXiv (Cornell University)

In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings. In particular, we propose a learning procedure that …

Unsupervised Bilingual Lexicon Induction Across Writing Systems

2020 · arXiv (Cornell University)

Recent embedding-based methods in unsupervised bilingual lexicon induction have shown good results, but generally have not leveraged orthographic (spelling) information, which can be helpful for pairs of related languages. This work augments a state-of-the-art method …

Leveraging Context Information for Natural Question Generation

2018

Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

N-ary Relation Extraction using Graph-State LSTM

2018

Cross-sentence n-ary relation extraction detects relations among n entities across multiple sentences. Typical methods formulate an input as a document graph, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph …

Semantic Neural Machine Translation Using AMR

2019 · Transactions of the Association for Computational Linguistics

Abstract It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation …

A Graph-to-Sequence Model for AMR-to-Text Generation

2018

The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. …