33,161 research outputs found
Distributional semantics and machine learning for statistical machine translation
[EU]Lan honetan semantika distribuzionalaren eta ikasketa automatikoaren erabilera aztertzen
dugu itzulpen automatiko estatistikoa hobetzeko. Bide horretan, erregresio logistikoan
oinarritutako ikasketa automatikoko eredu bat proposatzen dugu hitz-segiden itzulpen-
probabilitatea modu dinamikoan modelatzeko. Proposatutako eredua itzulpen automatiko
estatistikoko ohiko itzulpen-probabilitateen orokortze bat dela frogatzen dugu, eta testuinguruko nahiz semantika distribuzionaleko informazioa barneratzeko baliatu ezaugarri
lexiko, hitz-cluster eta hitzen errepresentazio bektorialen bidez. Horretaz gain, semantika
distribuzionaleko ezagutza itzulpen automatiko estatistikoan txertatzeko beste hurbilpen
bat lantzen dugu: hitzen errepresentazio bektorial elebidunak erabiltzea hitz-segiden
itzulpenen antzekotasuna modelatzeko. Gure esperimentuek proposatutako ereduen baliagarritasuna erakusten dute, emaitza itxaropentsuak eskuratuz oinarrizko sistema sendo
baten gainean. Era berean, gure lanak ekarpen garrantzitsuak egiten ditu errepresentazio
bektorialen mapaketa elebidunei eta hitzen errepresentazio bektorialetan oinarritutako
hitz-segiden antzekotasun neurriei dagokienean, itzulpen automatikoaz haratago balio
propio bat dutenak semantika distribuzionalaren arloan.[EN]In this work, we explore the use of distributional semantics and machine learning to
improve statistical machine translation. For that purpose, we propose the use of a logistic
regression based machine learning model for dynamic phrase translation probability mod-
eling. We prove that the proposed model can be seen as a generalization of the standard
translation probabilities used in statistical machine translation, and use it to incorporate
context and distributional semantic information through lexical, word cluster and word
embedding features. Apart from that, we explore the use of word embeddings for phrase
translation probability scoring as an alternative approach to incorporate distributional
semantic knowledge into statistical machine translation. Our experiments show the
effectiveness of the proposed models, achieving promising results over a strong baseline.
At the same time, our work makes important contributions in relation to bilingual word
embedding mappings and word embedding based phrase similarity measures, which go be-
yond machine translation and have an intrinsic value in the field of distributional semantics
Conversational Word Embedding for Retrieval-Based Dialog System
Human conversations contain many types of information, e.g., knowledge,
common sense, and language habits. In this paper, we propose a conversational
word embedding method named PR-Embedding, which utilizes the conversation pairs
to learn word embedding. Different
from previous works, PR-Embedding uses the vectors from two different semantic
spaces to represent the words in post and reply. To catch the information among
the pair, we first introduce the word alignment model from statistical machine
translation to generate the cross-sentence window, then train the embedding on
word-level and sentence-level. We evaluate the method on single-turn and
multi-turn response selection tasks for retrieval-based dialog systems. The
experiment results show that PR-Embedding can improve the quality of the
selected response. PR-Embedding source code is available at
https://github.com/wtma/PR-EmbeddingComment: To appear at ACL 202
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
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