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Found 28 papers, 10 papers with code
On the Evaluation of Machine Translation n-best Lists
The first and principal contribution is an evaluation measure that characterizes the translation quality of an entire n-best list by asking whether many of the valid translations are placed near the top of the list.
Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?
We propose VocADT, a novel method for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model's weights fixed.
X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale
In this paper, we prioritize quality over scaling number of languages, with a focus on multilingual machine translation task, and introduce X-ALMA, a model designed with a commitment to ensuring top-tier performance across 50 diverse languages, regardless of their resource levels.
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy
Selecting an automatic metric that best emulates human annotators is often non-trivial, because there is no clear definition of "best emulates."
On-the-Fly Fusion of Large Language Models and Machine Translation
We propose the on-the-fly ensembling of a machine translation model with an LLM, prompted on the same task and input.
SOTASTREAM: A Streaming Approach to Machine Translation Training
Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer.
How to Choose How to Choose Your Chatbot: A Massively Multi-System MultiReference Data Set for Dialog Metric Evaluation
We release MMSMR, a Massively Multi-System MultiReference dataset to enable future work on metrics and evaluation for dialog.
Doubly-Trained Adversarial Data Augmentation for Neural Machine Translation
Neural Machine Translation (NMT) models are known to suffer from noisy inputs.
SMRT Chatbots: Improving Non-Task-Oriented Dialog with Simulated Multiple Reference Training
Non-task-oriented dialog models suffer from poor quality and non-diverse responses.
Measuring the `I don't know' Problem through the Lens of Gricean Quantity
We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice's Maxims of Conversation (1975).
The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education
This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE).
Simulated Multiple Reference Training Improves Low-Resource Machine Translation
Many valid translations exist for a given sentence, yet machine translation (MT) is trained with a single reference translation, exacerbating data sparsity in low-resource settings.
HABLex: Human Annotated Bilingual Lexicons for Experiments in Machine Translation
Bilingual lexicons are valuable resources used by professional human translators.
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
Continued training is an effective method for domain adaptation in neural machine translation.
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting
Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting.
An Empirical Exploration of Curriculum Learning for Neural Machine Translation
Machine translation systems based on deep neural networks are expensive to train.
The JHU Parallel Corpus Filtering Systems for WMT 2018
This work describes our submission to the WMT18 Parallel Corpus Filtering shared task.
Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering
We posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1{\%} and 10{\%} of high-quality data to be used to train machine translation systems.
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
To better understand the effectiveness of continued training, we analyze the major components of a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component's contribution to, and capacity for, domain adaptation.
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
Supervised domain adaptation{---}where a large generic corpus and a smaller in-domain corpus are both available for training{---}is a challenge for neural machine translation (NMT).
On the Impact of Various Types of Noise on Neural Machine Translation
We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems.
Neural Lattice Search for Domain Adaptation in Machine Translation
Domain adaptation is a major challenge for neural machine translation (NMT).
Paradigm Completion for Derivational Morphology
The generation of complex derived word forms has been an overlooked problem in NLP; we fill this gap by applying neural sequence-to-sequence models to the task.
Deep Generalized Canonical Correlation Analysis
We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other.
