161 research outputs found
MooseNet: A Trainable Metric for Synthesized Speech with a PLDA Module
We present MooseNet, a trainable speech metric that predicts the listeners'
Mean Opinion Score (MOS). We propose a novel approach where the Probabilistic
Linear Discriminative Analysis (PLDA) generative model is used on top of an
embedding obtained from a self-supervised learning (SSL) neural network (NN)
model. We show that PLDA works well with a non-finetuned SSL model when trained
only on 136 utterances (ca. one minute training time) and that PLDA
consistently improves various neural MOS prediction models, even
state-of-the-art models with task-specific fine-tuning. Our ablation study
shows PLDA training superiority over SSL model fine-tuning in a low-resource
scenario. We also improve SSL model fine-tuning using a convenient optimizer
choice and additional contrastive and multi-task training objectives. The
fine-tuned MooseNet NN with the PLDA module achieves the best results,
surpassing the SSL baseline on the VoiceMOS Challenge data.Comment: Accepted to SSW 12: https://openreview.net/forum?id=V6RZk6RzS
RankME: Reliable Human Ratings for Natural Language Generation
Human evaluation for natural language generation (NLG) often suffers from
inconsistent user ratings. While previous research tends to attribute this
problem to individual user preferences, we show that the quality of human
judgements can also be improved by experimental design. We present a novel
rank-based magnitude estimation method (RankME), which combines the use of
continuous scales and relative assessments. We show that RankME significantly
improves the reliability and consistency of human ratings compared to
traditional evaluation methods. In addition, we show that it is possible to
evaluate NLG systems according to multiple, distinct criteria, which is
important for error analysis. Finally, we demonstrate that RankME, in
combination with Bayesian estimation of system quality, is a cost-effective
alternative for ranking multiple NLG systems.Comment: Accepted to NAACL 2018 (The 2018 Conference of the North American
Chapter of the Association for Computational Linguistics
The E2E Dataset: New Challenges For End-to-End Generation
This paper describes the E2E data, a new dataset for training end-to-end,
data-driven natural language generation systems in the restaurant domain, which
is ten times bigger than existing, frequently used datasets in this area. The
E2E dataset poses new challenges: (1) its human reference texts show more
lexical richness and syntactic variation, including discourse phenomena; (2)
generating from this set requires content selection. As such, learning from
this dataset promises more natural, varied and less template-like system
utterances. We also establish a baseline on this dataset, which illustrates
some of the difficulties associated with this data.Comment: Accepted as a short paper for SIGDIAL 2017 (final submission
including supplementary material
Neural Response Ranking for Social Conversation: A Data-Efficient Approach
The overall objective of 'social' dialogue systems is to support engaging,
entertaining, and lengthy conversations on a wide variety of topics, including
social chit-chat. Apart from raw dialogue data, user-provided ratings are the
most common signal used to train such systems to produce engaging responses. In
this paper we show that social dialogue systems can be trained effectively from
raw unannotated data. Using a dataset of real conversations collected in the
2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good'
system responses to user utterances, i.e. responses which are likely to lead to
long and engaging conversations. We show that (1) our neural ranker
consistently outperforms several strong baselines when trained to optimise for
user ratings; (2) when trained on larger amounts of data and only using
conversation length as the objective, the ranker performs better than the one
trained using ratings -- ultimately reaching a Precision@1 of 0.87. This
advance will make data collection for social conversational agents simpler and
less expensive in the future.Comment: 2018 EMNLP Workshop SCAI: The 2nd International Workshop on
Search-Oriented Conversational AI. Brussels, Belgium, October 31, 201
Findings of the E2E NLG Challenge
This paper summarises the experimental setup and results of the first shared
task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue
systems. Recent end-to-end generation systems are promising since they reduce
the need for data annotation. However, they are currently limited to small,
delexicalised datasets. The E2E NLG shared task aims to assess whether these
novel approaches can generate better-quality output by learning from a dataset
containing higher lexical richness, syntactic complexity and diverse discourse
phenomena. We compare 62 systems submitted by 17 institutions, covering a wide
range of approaches, including machine learning architectures -- with the
majority implementing sequence-to-sequence models (seq2seq) -- as well as
systems based on grammatical rules and templates.Comment: Accepted to INLG 201
Data-driven Natural Language Generation: Paving the Road to Success
We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughly analysing current evaluation metrics and motivating the need for a
new, more reliable metric. The second problem is addressed by presenting a
novel framework for developing and evaluating a high quality corpus for NLG
training.Comment: WiNLP workshop at ACL 201
Referenceless Quality Estimation for Natural Language Generation
Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate
Natural Language (LGNL), Sydney, 10 August 201
With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector
This work presents our efforts to reproduce the results of the human
evaluation experiment presented in the paper of Vamvas and Sennrich (2022),
which evaluated an automatic system detecting over- and undertranslations
(translations containing more or less information than the original) in machine
translation (MT) outputs. Despite the high quality of the documentation and
code provided by the authors, we discuss some problems we found in reproducing
the exact experimental setup and offer recommendations for improving
reproducibility. Our replicated results generally confirm the conclusions of
the original study, but in some cases, statistically significant differences
were observed, suggesting a high variability of human annotation.Comment: Submitted to
https://www.aclweb.org/portal/content/repronlp-shared-task-reproducibility-evaluations-nlp-202
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
We present three enhancements to existing encoder-decoder models for
open-domain conversational agents, aimed at effectively modeling coherence and
promoting output diversity: (1) We introduce a measure of coherence as the
GloVe embedding similarity between the dialogue context and the generated
response, (2) we filter our training corpora based on the measure of coherence
to obtain topically coherent and lexically diverse context-response pairs, (3)
we then train a response generator using a conditional variational autoencoder
model that incorporates the measure of coherence as a latent variable and uses
a context gate to guarantee topical consistency with the context and promote
lexical diversity. Experiments on the OpenSubtitles corpus show a substantial
improvement over competitive neural models in terms of BLEU score as well as
metrics of coherence and diversity
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