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Found 19 papers, 6 papers with code
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation
Building neural machine translation systems to perform well on a specific target domain is a well-studied problem.
A Preference-driven Paradigm for Enhanced Translation with Large Language Models
Recent research has shown that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) using only a small amount of parallel data.
Trained MT Metrics Learn to Cope with Machine-translated References
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood.
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation
Customer feedback can be an important signal for improving commercial machine translation systems.
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation
A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as "under the weather".
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation
In the Japanese language different levels of honorific speech are used to convey respect, deference, humility, formality and social distance.
Neural Machine Translation Decoding with Terminology Constraints
Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem.
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers.
A Comparison of Neural Models for Word Ordering
We compare several language models for the word-ordering task and propose a new bag-to-sequence neural model based on attention-based sequence-to-sequence models.
SGNMT -- A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies
This paper introduces SGNMT, our experimental platform for machine translation research.
Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices
This makes our approach much more flexible than $n$-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space.
The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16
This paper presents the University of Cambridge submission to WMT16.
Syntactically Guided Neural Machine Translation
We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT).
Multilingual Image Description with Neural Sequence Models
In this paper we present an approach to multi-language image description bringing together insights from neural machine translation and neural image description.
