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Found 34 papers, 7 papers with code
Data Strategies for Low-Resource Grammatical Error Correction
We investigate how best to take advantage of existing data sources for improving GEC systems for languages with limited quantities of high quality training data.
Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large Language Models
One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations.
Towards an On-device Agent for Text Rewriting
Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting.
Improved Long-Form Spoken Language Translation with Large Language Models
A challenge in spoken language translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations.
Conciseness: An Overlooked Language Task
We report on novel investigations into training models that make sentences concise.
Text Generation with Text-Editing Models
Text-editing models have recently become a prominent alternative to seq2seq models for monolingual text-generation tasks such as grammatical error correction, simplification, and style transfer.
Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES
The softmax layer in neural machine translation is designed to model the distribution over mutually exclusive tokens.
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models
In many natural language processing (NLP) tasks the same input (e. g. source sentence) can have multiple possible outputs (e. g. translations).
Transformer-based Models of Text Normalization for Speech Applications
Text normalization, or the process of transforming text into a consistent, canonical form, is crucial for speech applications such as text-to-speech synthesis (TTS).
Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models
Synthetic data generation is widely known to boost the accuracy of neural grammatical error correction (GEC) systems, but existing methods often lack diversity or are too simplistic to generate the broad range of grammatical errors made by human writers.
Seq2Edits: Sequence Transduction Using Span-level Edit Operations
For text normalization, sentence fusion, and grammatical error correction, our approach improves explainability by associating each edit operation with a human-readable tag.
Using Context in Neural Machine Translation Training Objectives
We find that each of these lines of research has a clear space in it for the other, and propose merging them with a scheme that allows a document-level evaluation metric to be used in the NMT training objective.
Neural Machine Translation: A Review and Survey
The field of machine translation (MT), the automatic translation of written text from one natural language into another, has experienced a major paradigm shift in recent years.
On NMT Search Errors and Model Errors: Cat Got Your Tongue?
We report on search errors and model errors in neural machine translation (NMT).
CUED@WMT19:EWC\&LMs
Two techniques provide the fabric of the Cambridge University Engineering Department{'}s (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).
Neural and FST-based approaches to grammatical error correction
In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction.
The CUED's Grammatical Error Correction Systems for BEA-2019
We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction.
UCAM Biomedical translation at WMT19: Transfer learning multi-domain ensembles
The 2019 WMT Biomedical translation task involved translating Medline abstracts.
Cued@wmt19:ewc&lms
Two techniques provide the fabric of the Cambridge University Engineering Department's (CUED) entry to the WMT19 evaluation campaign: elastic weight consolidation (EWC) and different forms of language modelling (LMs).
Domain Adaptive Inference for Neural Machine Translation
We investigate adaptive ensemble weighting for Neural Machine Translation, addressing the case of improving performance on a new and potentially unknown domain without sacrificing performance on the original domain.
Neural Models of Text Normalization for Speech Applications
One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS).
Neural Grammatical Error Correction with Finite State Transducers
Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data.
Simple Fusion: Return of the Language Model
Neural Machine Translation (NMT) typically leverages monolingual data in training through backtranslation.
An Operation Sequence Model for Explainable Neural Machine Translation
We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself.
The University of Cambridge's Machine Translation Systems for WMT18
The University of Cambridge submission to the WMT18 news translation task focuses on the combination of diverse models of translation.
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT
We explore strategies for incorporating target syntax into Neural Machine Translation.
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models.
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.
Unfolding and Shrinking Neural Machine Translation Ensembles
Ensembling is a well-known technique in neural machine translation (NMT) to improve system performance.
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).
