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We have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages

A Comparative Analysis of Unsupervised Language Adaptation Methods.

DeepLo@EMNLP-IJCNLP, pp.11-21, (2019)

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Abstract

To overcome the lack of annotated resources in less-resourced languages, recent approaches have been proposed to perform unsupervised language adaptation. In this paper, we explore three recent proposals: Adversarial Training, Sentence Encoder Alignment and SharedPrivate Architecture. We highlight the differences of these approaches in te...More

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Introduction
  • Proposed approaches for unsupervised adaptation have been explored in a variety of machine learning domains, including image recognition (Ganin and Lempitsky, 2015; Bousmalis et al, 2016) and natural language processing (Chen et al, 2018; Conneau et al, 2018).

    In unsupervised language adaptation, annotated resources on a source language (S) are available, in the form XS, YS.
  • The authors explore two different approaches that leverage parallel data: a Sentence Encoder Alignment (Section 4.2) (Conneau et al, 2018) and a Shared-Private Architecture (Section 4.3) (Bousmalis et al, 2016)
  • The authors select these approaches from many recent proposals because they differ on the main axis of the analysis, they approach the problem using conceptually different methods, and they correspond to state-of-the-art approaches
Highlights
  • Proposed approaches for unsupervised adaptation have been explored in a variety of machine learning domains, including image recognition (Ganin and Lempitsky, 2015; Bousmalis et al, 2016) and natural language processing (Chen et al, 2018; Conneau et al, 2018).

    In unsupervised language adaptation, annotated resources on a source language (S) are available, in the form XS, YS
  • Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo), pages 11–21 Hong Kong, China, November 3, 2019. c 2019 Association for Computational Linguistics https://doi.org/10.18653/v1/P17 marised as follows: (a) we divide and analyse proposed approaches for unsupervised language adaptation by taking into account their assumptions on available resources; (b) for the natural language inference (NLI) task, we explore adversarial training approaches and provide a new baseline for sentence encoders without requiring parallel data
  • To evaluate the methods described in Section 4 for unsupervised cross-lingual settings, we report on experiments performed on two different tasks: Natural Language Inference and Sentiment Classification
  • Experimental results for the Natural Language Inference (NLI) task are shown in Table 1
  • We have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages
  • Our results indicate that the characteristics of the datasets used in the source language and on the target language are an important factor to consider when choosing the architecture to employ
Methods
  • To address the task of unsupervised language adaptation, the authors explore three approaches: Adversarial Training (Section 4.1), Sentence Encoder Alignment (Section 4.2), and Shared-Private Architecture (Section 4.3).
  • By unsupervised language adaptation the authors consider that during the training phase the model is fed with labeled data on the source language and that no labeled data on target language is available.
  • To train the model on a cross-lingual setting, unlabeled data on the source and target language are provided.
  • The remaining two approaches require parallel sentences for the source and target languages
Results
  • Experimental results for the

    NLI task are shown in Table 1.
  • Conneau et al (2018)), the authors can observe that the scores are lower.
  • The authors attribute this to some parameter choices that were driven by computational efficiency concerns.
  • The authors focus the work on a comparison between different architectures and, the authors aim at a comparative analysis between those architecture in similar settings
Conclusion
  • Conclusions and Future Work

    The authors have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages.

    The authors' results indicate that the characteristics of the datasets used in the source language and on the target language are an important factor to consider when choosing the architecture to employ.
  • The authors have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages.
  • On the other hand, when the source and target language datasets have the same characteristics, sentence alignment approaches are very effective and obtain scores in the target language that are closer to source language scores.
  • Hyper-parameter tuning of different loss components is a challenging task that the authors aim to study in more detail
Summary
  • Introduction:

    Proposed approaches for unsupervised adaptation have been explored in a variety of machine learning domains, including image recognition (Ganin and Lempitsky, 2015; Bousmalis et al, 2016) and natural language processing (Chen et al, 2018; Conneau et al, 2018).

    In unsupervised language adaptation, annotated resources on a source language (S) are available, in the form XS, YS.
  • The authors explore two different approaches that leverage parallel data: a Sentence Encoder Alignment (Section 4.2) (Conneau et al, 2018) and a Shared-Private Architecture (Section 4.3) (Bousmalis et al, 2016)
  • The authors select these approaches from many recent proposals because they differ on the main axis of the analysis, they approach the problem using conceptually different methods, and they correspond to state-of-the-art approaches
  • Objectives:

    In the language discriminator component, the authors aim to minimize the negative log-likelihood of the ground truth language discrimination for each input sequence in xmix, where xmix corresponds to a balanced sample of sentences randomly taken from both source and target language datasets.
  • The authors aim to explore recent advances made on multilingual contextualized word embeddings and determine whether they impact the results reported in this work.
  • Hyper-parameter tuning of different loss components is a challenging task that the authors aim to study in more detail
  • Methods:

    To address the task of unsupervised language adaptation, the authors explore three approaches: Adversarial Training (Section 4.1), Sentence Encoder Alignment (Section 4.2), and Shared-Private Architecture (Section 4.3).
  • By unsupervised language adaptation the authors consider that during the training phase the model is fed with labeled data on the source language and that no labeled data on target language is available.
  • To train the model on a cross-lingual setting, unlabeled data on the source and target language are provided.
  • The remaining two approaches require parallel sentences for the source and target languages
  • Results:

    Experimental results for the

    NLI task are shown in Table 1.
  • Conneau et al (2018)), the authors can observe that the scores are lower.
  • The authors attribute this to some parameter choices that were driven by computational efficiency concerns.
  • The authors focus the work on a comparison between different architectures and, the authors aim at a comparative analysis between those architecture in similar settings
  • Conclusion:

    Conclusions and Future Work

    The authors have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages.

    The authors' results indicate that the characteristics of the datasets used in the source language and on the target language are an important factor to consider when choosing the architecture to employ.
  • The authors have studied unsupervised language adaptation approaches on two natural language processing tasks, taking into consideration the assumptions made regarding the availability of unlabeled data in the source and target languages.
  • On the other hand, when the source and target language datasets have the same characteristics, sentence alignment approaches are very effective and obtain scores in the target language that are closer to source language scores.
  • Hyper-parameter tuning of different loss components is a challenging task that the authors aim to study in more detail
Tables
  • Table1: XNLI accuracy scores
  • Table2: Sentiment Classification accuracy scores languages. Against our intuition, the SharedPrivate Architecture presents a considerable drop of performance when compared with the Sentence Encoder Alignment method even if the sentence encoder alignment procedure is also performed in the former (i.e. Lsim = Lalign). We attribute this to the reduced number of updates that is performed for the alignment procedure in the Shared-Private Architecture (given that we compute a joint loss, the number of iterations is determined by the size of the labeled data for the task at hand). On the other hand, the Sentence Encoder Alignment method can make complete use of the 2 million parallel sentences. We also studied the capability of the shared and private feature extractors to predict the language of a given set of input sequences. After some epochs of training, we observe that the shared feature extractor is unable to distinguish the input sequence language (obtaining 50% of accuracy to distinguish the languages). On the other hand, the private feature extractor masters the task reaching an accuracy of approximately 100%
Download tables as Excel
Related work
  • The Natural Language Inference (NLI) task has emerged as one of the main tasks to evaluate NLP systems for sentence understanding. Given two text fragments, “Text” (T ) and “Hypothesis” (H), NLI is the task of determining whether the meaning of H is in an entailment, contradiction or neither (neutral) relation to the text fragment T . Consequently, this task is framed in a 3-way classification setting (Dagan et al, 2013).

    State-of-the-art systems explore complex sentence encoding techniques using a variety of approaches, such as recurrent (Bowman et al, 2015a) and recursive (Bowman et al, 2015b) neural networks. To capture the relations between the text and hypothesis, sentence aggregation functions (Chen et al, 2017; Peters et al, 2018) and attention mechanisms (Rocktaschel et al, 2016) have been successfully applied to address the task. On the cross-lingual setting, there has been work using parallel corpora (Mehdad et al, 2011) and lexical resources (Castillo, 2011), as well as shared tasks (Camacho-Collados et al, 2017). Most of these systems rely heavily on the availability of multilingual resources (e.g. bilingual dictionaries) and on machine translation systems to explore projection (Yarowsky et al, 2001) or direct transfer (McDonald et al, 2011) approaches. Recently, a large-scale corpus for NLI for 15 languages was released (details in Section 3) together with multilingual sentence encoders baselines (Conneau et al, 2018). More recently, new methods to train language models provided the ground basis for contextualized word embeddings (Peters et al, 2018), which constitute the new state-of-art in several tasks, including the NLI and XNLI tasks (Devlin et al, 2019; Lample and Conneau, 2019). In this paper, we constraint our work to the conventional (cross-lingual) word embeddings (Ruder, 2017) that have been widely used and focus on a comparative analysis between different approaches for unsupervised language adaptation. We leave the study of the effects of this recent line of work on our analysis as future work.
Funding
  • Gil Rocha is supported by a PhD scholarship (SFRH/BD/140125/2018) from Fundacao para a Ciencia e a Tecnologia (FCT)
  • This research is supported by project DARGMINTS (POCI/01/0145/FEDER/031460), funded by FCT
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