Context-aware graph segmentation for graph-based translation

In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations leftto-right using beam search. Experiments on Chinese–English and German–English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model

Combining translation memories and syntax-based SMT: experiments with real industrial data

One major drawback of using Translation Memories (TMs) in phrase-based Machine Translation (MT) is that only continuous phrases are considered. In contrast, syntax-based MT allows phrasal discontinuity by learning translation rules containing non-terminals. In this paper, we combine a TM with syntax-based MT via sparse features. These features are extracted during decoding based on translation rules and their corresponding patterns in the TM. We have tested this approach by carrying out experiments on real English–Spanish industrial data. Our results show that these TM features significantly improve syntax-based MT. Our final system yields improvements of up to +3.1 BLEU, +1.6 METEOR, and -2.6 TER when compared with a stateof-the-art phrase-based MT system

Topic-informed neural machine translation

In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information

ProphetMT: controlled language authoring aid system description

This paper presents ProphetMT, a monolingual Controlled Language (CL) authoring tool which allows users to easily compose an in-domain sentence with the help of tree-based SMT-driven auto-suggestions. The interface also visualizes target-language sentences as they are built by the SMT system. When the user is finished composing, the final translation(s) are generated by a tree-based SMT system using the text and structural information provided by the user. With this domain-specific controlled language, ProphetMT will produce highly reliable translations. The contributions of this work are: 1) we develop a user-friendly auto-completion-based editor which guarantees that the vocabulary and grammar chosen by a user are compatible with a tree-based SMT model; 2) by applying a shift-reduce-like parsing feature, this editor allows users to write from left-to-right and generates the parsing results on the fly. Accordingly, with this in-domain composing restriction as well as the gold-standard parsing result, a highly reliable translation can be generated

SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme

Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies. In this work, we bridge this practical gap by proposing a song rewriting system which rewrites the lyrics of an existing song such that the generated lyrics are compatible with the rhythm of the existing melody and thus singable. In particular, we propose SongRewriter, a controllable Chinese lyric generation and editing system which assists users without prior knowledge of melody composition. The system is trained by a randomized multi-level masking strategy which produces a unified model for generating entirely new lyrics or editing a few fragments. To improve the controllabiliy of the generation process, we further incorporate a keyword prompt to control the lexical choices of the content and propose novel decoding constraints and a vowel modeling task to enable flexible end and internal rhyme schemes. While prior rhyming metrics are mainly for rap lyrics, we propose three novel rhyming evaluation metrics for song lyrics. Both automatic and human evaluations show that the proposed model performs better than the state-of-the-art models in both contents and rhyming quality. Our code and models implemented in MindSpore Lite tool will be available