Shuming Shi

Target Foresight Based Attention for Neural Machine Translation

2018

Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, Max Meng. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Graph Based Translation Memory for Neural Machine Translation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

A translation memory (TM) is proved to be helpful to improve neural machine translation (NMT). Existing approaches either pursue the decoding efficiency by merely accessing local information in a TM or encode the global information …

TranSmart: A Practical Interactive Machine Translation System

2021 · arXiv (Cornell University)

Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed. This technique report introduces TranSmart, a practical human-machine interactive translation system that is able to trade off translation quality and …

Rethinking Word-Level Auto-Completion in Computer-Aided Translation

2023 · arXiv (Cornell University)

Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation. It aims at providing word-level auto-completion suggestions for human translators. While previous studies have primarily focused on designing complex model architectures, this paper takes a …

Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model

2024

Zhiwei He, Xing Wang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang, Shuming Shi, Zhaopeng Tu. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: …

How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation

2016

Recently a few systems for automatically solving math word problems have reported promising results. However, the datasets used for evaluation have limitations in both scale and diversity. In this paper, we build a large-scale dataset …

Deep Neural Solver for Math Word Problems

2017

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) …

Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings

2018

Yan Song, Shuming Shi, Jing Li, Haisong Zhang. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018.

On the Word Alignment from Neural Machine Translation

2019

Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, this paper finds attention may almost fail to capture word alignment for some NMT models. This paper thereby proposes …

Learning to Remember Translation History with a Continuous Cache

2018 · Transactions of the Association for Computational Linguistics

Existing neural machine translation (NMT) models generally translate sentences in isolation, missing the opportunity to take advantage of document-level information. In this work, we propose to augment NMT models with a very light-weight cache-like memory …