364 research outputs found
Trivial Transfer Learning for Low-Resource Neural Machine Translation
Transfer learning has been proven as an effective technique for neural
machine translation under low-resource conditions. Existing methods require a
common target language, language relatedness, or specific training tricks and
regimes. We present a simple transfer learning method, where we first train a
"parent" model for a high-resource language pair and then continue the training
on a lowresource pair only by replacing the training corpus. This "child" model
performs significantly better than the baseline trained for lowresource pair
only. We are the first to show this for targeting different languages, and we
observe the improvements even for unrelated languages with different alphabets.Comment: Accepted to WMT18 reseach paper, Proceedings of the 3rd Conference on
Machine Translation 201
Analyzing Error Types in English-Czech Machine Translation
This paper examines two techniques of manual evaluation that can be used to identify error
types of individual machine translation systems. The first technique of “blind post-editing” is
being used in WMT evaluation campaigns since 2009 and manually constructed data of this
type are available for various language pairs. The second technique of explicit marking of errors
has been used in the past as well.
We propose a method for interpreting blind post-editing data at a finer level and compare
the results with explicit marking of errors. While the human annotation of either of the techniques
is not exactly reproducible (relatively low agreement), both techniques lead to similar
observations of differences of the systems. Specifically, we are able to suggest which errors in
MT output are easy and hard to correct with no access to the source, a situation experienced by
users who do not understand the source language
Are BLEU and Meaning Representation in Opposition?
One of possible ways of obtaining continuous-space sentence representations
is by training neural machine translation (NMT) systems. The recent attention
mechanism however removes the single point in the neural network from which the
source sentence representation can be extracted. We propose several variations
of the attentive NMT architecture bringing this meeting point back. Empirical
evaluation suggests that the better the translation quality, the worse the
learned sentence representations serve in a wide range of classification and
similarity tasks.Comment: ACL 2018; 10 pages + 2 page supplementar
Are BLEU and Meaning Representation in Opposition?
One of possible ways of obtaining continuous-space sentence representations
is by training neural machine translation (NMT) systems. The recent attention
mechanism however removes the single point in the neural network from which the
source sentence representation can be extracted. We propose several variations
of the attentive NMT architecture bringing this meeting point back. Empirical
evaluation suggests that the better the translation quality, the worse the
learned sentence representations serve in a wide range of classification and
similarity tasks.Comment: ACL 2018; 10 pages + 2 page supplementar
Giving a Sense: A Pilot Study in Concept Annotation from Multiple Resources
We present a pilot study of a web-based annotation of words with senses. The annotated senses come from several knowledge bases and sense inventories. The study is the first step in a planned larger annotation of grounding and should allow us to select a subset of the sense sources that cover any given text reasonably well and show an acceptable level of inter-annotator agreement
Minuteman: Machine and Human Joining Forces in Meeting Summarization
Many meetings require creating a meeting summary to keep everyone up to date.
Creating minutes of sufficient quality is however very cognitively demanding.
Although we currently possess capable models for both audio speech recognition
(ASR) and summarization, their fully automatic use is still problematic. ASR
models frequently commit errors when transcribing named entities while the
summarization models tend to hallucinate and misinterpret the transcript. We
propose a novel tool -- Minuteman -- to enable efficient semi-automatic meeting
minuting. The tool provides a live transcript and a live meeting summary to the
users, who can edit them in a collaborative manner, enabling correction of ASR
errors and imperfect summary points in real time. The resulting application
eases the cognitive load of the notetakers and allows them to easily catch up
if they missed a part of the meeting due to absence or a lack of focus. We
conduct several tests of the application in varied settings, exploring the
worthiness of the concept and the possible user strategies.Comment: 6 pages, 3 figure
Boosting Unsupervised Machine Translation with Pseudo-Parallel Data
Even with the latest developments in deep learning and large-scale language
modeling, the task of machine translation (MT) of low-resource languages
remains a challenge. Neural MT systems can be trained in an unsupervised way
without any translation resources but the quality lags behind, especially in
truly low-resource conditions. We propose a training strategy that relies on
pseudo-parallel sentence pairs mined from monolingual corpora in addition to
synthetic sentence pairs back-translated from monolingual corpora. We
experiment with different training schedules and reach an improvement of up to
14.5 BLEU points (English to Ukrainian) over a baseline trained on
back-translated data only.Comment: MT Summit 202
The Design of Eman, an Experiment Manager
We present eman, a tool for managing large numbers of computational experiments. Over
the years of our research in machine translation (MT), we have collected a couple of ideas for
efficient experimenting. We believe these ideas are generally applicable in (computational)
research of any field. We incorporated them into eman in order to make them available in a
command-line Unix environment.
The aim of this article is to highlight the core of the many ideas. We hope the text can serve
as a collection of experiment management tips and tricks for anyone, regardless their field of
study or computer platform they use. The specific examples we provide in eman’s current syntax
are less important but they allow us to use concrete terms. The article thus also fills the gap in eman documentation by providing some high-level overview
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