908 research outputs found
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemmatised and PoS-tagged form. We provided four subtasks, for the English–French and English–German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English–French subtask, five for French–English, nine for English–German, and six for German–English. Most of the submissions outperformed two strong language-model-based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.</jats:p
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Linguistic typology aims to capture structural and semantic variation across
the world's languages. A large-scale typology could provide excellent guidance
for multilingual Natural Language Processing (NLP), particularly for languages
that suffer from the lack of human labeled resources. We present an extensive
literature survey on the use of typological information in the development of
NLP techniques. Our survey demonstrates that to date, the use of information in
existing typological databases has resulted in consistent but modest
improvements in system performance. We show that this is due to both intrinsic
limitations of databases (in terms of coverage and feature granularity) and
under-employment of the typological features included in them. We advocate for
a new approach that adapts the broad and discrete nature of typological
categories to the contextual and continuous nature of machine learning
algorithms used in contemporary NLP. In particular, we suggest that such
approach could be facilitated by recent developments in data-driven induction
of typological knowledge
Evaluating Discourse Phenomena in Neural Machine Translation
For machine translation to tackle discourse phenomena, models must have
access to extra-sentential linguistic context. There has been recent interest
in modelling context in neural machine translation (NMT), but models have been
principally evaluated with standard automatic metrics, poorly adapted to
evaluating discourse phenomena. In this article, we present hand-crafted,
discourse test sets, designed to test the models' ability to exploit previous
source and target sentences. We investigate the performance of recently
proposed multi-encoder NMT models trained on subtitles for English to French.
We also explore a novel way of exploiting context from the previous sentence.
Despite gains using BLEU, multi-encoder models give limited improvement in the
handling of discourse phenomena: 50% accuracy on our coreference test set and
53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A
simple strategy of decoding the concatenation of the previous and current
sentence leads to good performance, and our novel strategy of multi-encoding
and decoding of two sentences leads to the best performance (72.5% for
coreference and 57% for coherence/cohesion), highlighting the importance of
target-side context.Comment: Final version of paper to appear in Proceedings of NAACL 201
Coherence in Machine Translation
Coherence ensures individual sentences work together to form a meaningful document. When properly translated, a coherent document in one language should result in a coherent document in another language. In Machine Translation, however, due to reasons of modeling and computational complexity, sentences are pieced together from words or phrases based on short context windows and
with no access to extra-sentential context.
In this thesis I propose ways to automatically assess the coherence of machine translation output. The work is structured around three dimensions: entity-based coherence, coherence as evidenced via syntactic patterns, and coherence as
evidenced via discourse relations.
For the first time, I evaluate existing monolingual coherence models on this new task, identifying issues and challenges that are specific to the machine translation setting. In order to address these issues, I adapted a state-of-the-art syntax
model, which also resulted in improved performance for the monolingual task. The results clearly indicate how much more difficult the new task is than the task of detecting shuffled texts. I proposed a new coherence model, exploring the crosslingual transfer of discourse relations in machine translation. This model is novel in that it measures the correctness of the discourse relation by comparison to the source text rather than to a reference translation. I identified patterns of incoherence common across different language pairs, and created a corpus of machine translated output annotated with coherence errors for evaluation purposes. I then examined
lexical coherence in a multilingual context, as a preliminary study for crosslingual transfer. Finally, I determine how the new and adapted models correlate with human judgements of translation quality and suggest that improvements in general evaluation within machine translation would benefit from having a coherence component that evaluated the translation output with respect to the source text
A Survey on Semantic Processing Techniques
Semantic processing is a fundamental research domain in computational
linguistics. In the era of powerful pre-trained language models and large
language models, the advancement of research in this domain appears to be
decelerating. However, the study of semantics is multi-dimensional in
linguistics. The research depth and breadth of computational semantic
processing can be largely improved with new technologies. In this survey, we
analyzed five semantic processing tasks, e.g., word sense disambiguation,
anaphora resolution, named entity recognition, concept extraction, and
subjectivity detection. We study relevant theoretical research in these fields,
advanced methods, and downstream applications. We connect the surveyed tasks
with downstream applications because this may inspire future scholars to fuse
these low-level semantic processing tasks with high-level natural language
processing tasks. The review of theoretical research may also inspire new tasks
and technologies in the semantic processing domain. Finally, we compare the
different semantic processing techniques and summarize their technical trends,
application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN
1566-2535. The equal contribution mark is missed in the published version due
to the publication policies. Please contact Prof. Erik Cambria for detail
- …