Victor O. K. Li

Neural Machine Translation with Gumbel-Greedy Decoding

2017 · arXiv (Cornell University)

Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time. In this paper, we propose the …

Efficient Learning for Undirected Topic Models

2015

Jiatao Gu, Victor O.K. Li. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2015.

Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain …

Incorporating Copying Mechanism in Sequence-to-Sequence Learning

2016 · arXiv (Cornell University)

We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the output sequence. A similar phenomenon is observable in human …

Non-Autoregressive Neural Machine Translation

2017 · arXiv (Cornell University)

Existing approaches to neural machine translation condition each output word on previously generated outputs. We introduce a model that avoids this autoregressive property and produces its outputs in parallel, allowing an order of magnitude lower …

Universal Neural Machine Translation for Extremely Low Resource Languages

2018 · arXiv (Cornell University)

In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations …

Search Engine Guided Neural Machine Translation

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

In this paper, we extend an attention-based neural machine translation (NMT) model by allowing it to access an entire training set of parallel sentence pairs even after training. The proposed approach consists of two stages. …

Meta-Learning for Low-Resource Neural Machine Translation

2018

In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for lowresource neural machine translation (NMT). We frame low-resource translation as a metalearning problem, and we learn …