321 research outputs found
Cross-Lingual Propagation of Sentiment Information Based on Bilingual Vector Space Alignment
Deep learning methods have shown to be particularly effective in inferring the sentiment polarity of a text snippet. However, in cross-domain and cross-lingual scenarios there is often a lack of training data. To tackle this issue, propagation algorithms can be used to yield sentiment information for various languages and domains by transferring knowledge from a source language(usually English). To propagate polarity scores to the target language, these algorithms take as input an initial vocabulary and a bilingual lexicon. In this paper we propose to enrich lexicon in-formation for cross-lingual propagation by inferring the bilingual semantic relationships from an aligned bilingual vector space.This allows us to exploit the underlying text similarities that are not made explicit by the lexicon. The experiments show that our approach outperforms the state-of-the-art propagation method on multilingual datasets
Learning Bilingual Word Representations by Marginalizing Alignments
We present a probabilistic model that simultaneously learns alignments and
distributed representations for bilingual data. By marginalizing over word
alignments the model captures a larger semantic context than prior work relying
on hard alignments. The advantage of this approach is demonstrated in a
cross-lingual classification task, where we outperform the prior published
state of the art.Comment: Proceedings of ACL 2014 (Short Papers
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
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
As an extensive research in the field of Natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack of sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Specifically, we design two contrastive strategies, token
level contrastive learning of token embeddings (TL-CTE) and sentiment level
contrastive learning of token embeddings (SL-CTE), to regularize the semantic
space of source and target language to be more uniform. Since our framework can
receive datasets in multiple languages during training, our framework can be
adapted not only for XABSA task, but also for multilingual aspect-based
sentiment analysis (MABSA). To further improve the performance of our model, we
perform knowledge distillation technology leveraging data from unlabeled target
language. In the distillation XABSA task, we further explore the comparative
effectiveness of different data (source dataset, translated dataset, and
code-switched dataset). The results demonstrate that the proposed method has a
certain improvement in the three tasks of XABSA, distillation XABSA and MABSA.
For reproducibility, our code for this paper is available at
https://github.com/GKLMIP/CL-XABSA
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
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
A survey of cross-lingual word embedding models
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages. In this survey, we provide a comprehensive typology of cross-lingual word embedding models. We compare their data requirements and objective functions. The recurring theme of the survey is that many of the models presented in the literature optimize for the same objectives, and that seemingly different models are often equivalent, modulo optimization strategies, hyper-parameters, and such. We also discuss the different ways cross-lingual word embeddings are evaluated, as well as future challenges and research horizons.</jats:p
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
Semantic specialization is the process of fine-tuning pre-trained
distributional word vectors using external lexical knowledge (e.g., WordNet) to
accentuate a particular semantic relation in the specialized vector space.
While post-processing specialization methods are applicable to arbitrary
distributional vectors, they are limited to updating only the vectors of words
occurring in external lexicons (i.e., seen words), leaving the vectors of all
other words unchanged. We propose a novel approach to specializing the full
distributional vocabulary. Our adversarial post-specialization method
propagates the external lexical knowledge to the full distributional space. We
exploit words seen in the resources as training examples for learning a global
specialization function. This function is learned by combining a standard
L2-distance loss with an adversarial loss: the adversarial component produces
more realistic output vectors. We show the effectiveness and robustness of the
proposed method across three languages and on three tasks: word similarity,
dialog state tracking, and lexical simplification. We report consistent
improvements over distributional word vectors and vectors specialized by other
state-of-the-art specialization frameworks. Finally, we also propose a
cross-lingual transfer method for zero-shot specialization which successfully
specializes a full target distributional space without any lexical knowledge in
the target language and without any bilingual data.Comment: Accepted at EMNLP 201
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