United States

Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications. We present an empirical comparison of transformer-based text classification models in a variety of practical monolingual and multilingual pretraining and fine-tuning settings. We evaluate these methods on two distinct tasks in five different languages. Departing from prior work, our results show that multilingual language models can outperform monolingual ones in some downstream tasks and target languages. We additionally show that practical modifications such as task- and domain-adaptive pretraining and data augmentation can improve classification performance without the need for additional labeled data.

NAACL 2021

Practical Transformer-based Multilingual Text Classification

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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technical paper

Email and chat communication tools are increasingly important for completing daily tasks. Accurate real-time phrase completion can save time and bolster productivity. Modern text prediction algorithms are based on large language models which typically rely on the prior words in a message to predict a completion. We examine how additional contextual signals (from previous messages, time, and subject) affect the performance of a commercial text prediction model. We compare contextual text prediction in chat and email messages from two of the largest commercial platforms Microsoft Teams and Outlook, finding that contextual signals contribute to performance differently between these scenarios. On emails, time context is most beneficial with small relative gains of 2% over baseline. Whereas, in chat scenarios, using a tailored set of previous messages as context yields relative improvements over the baseline between 9.3% and 18.6% across various critical service-oriented text prediction metrics.

When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages

In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities. Previous works that incorporate context for SLU have mostly focused on domains where context is limited to a few minutes. However, there are domains that have related context that could span up to hours and days. In this paper, we propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. Experiments on the Contextual Domain Classification (CDC) task with various encoder architectures show that temporal representations combining both information outperforms only one of the two. We further demonstrate that our contextual Transformer is able to reduce 13.04% of classification errors compared to a non-contextual baseline. We also conduct empirical analyses to study recent versus distant context and opportunities to lower deployment costs.

Contextual Domain Classification with Temporal Representations

One of the first building blocks to create a voice assistant is the task of tagging entities or attributes in user queries. This can be particularly challenging when the number of entities are in the tenth of millions, as is the case of music catalogs. Training slot tagging models at an industrial scale requires large quantities of accurately labeled user queries, which are often hard and costly to gather. On the other hand, voice assistants typically collect plenty of unlabeled queries that often remain unexploited.

Bootstrapping a Music Voice Assistant with Weak Supervision

Scaling conversational personal assistants to a multitude of languages puts high demands on collecting and labelling data, a setting in which cross-lingual learning techniques can help to reconcile the need for well-performing natural language understanding (NLU) with a desideratum to support many languages without incurring unacceptable cost. In this paper, we show that automatically annotating unlabeled utterances using machine translation in an offline fashion and adding them to the training data can improve performance for existing NLU features for low-resource languages, where a straightforward translate-test approach as considered in existing literature would fail the latency requirements of a live environment. We demonstrate the effectiveness of our method with intrinsic and extrinsic evaluation using a real-world commercial dialog system in German. We show that 56% of the resulting automatically labeled utterances had a perfect match with ground-truth labels. Moreover, we see significant performance improvements in an extrinsic evaluation settings when manually labeled data is available in small quantities.

Continuous Model Improvement for Language Understanding with Machine Translation

Developing Text Normalization (TN) systems for Text-to-Speech (TTS) on new languages is hard. We propose a novel architecture to facilitate it for multiple languages while using data less than 3% of the size of the data used by the state of the art results on English. We treat TN as a sequence classification problem and propose a granular tokenization mechanism that enables the system to learn majority of the classes and their normalizations from the training data itself. This is further combined with minimal pre-coded linguistic knowledge for other classes. We publish the first results on TN for TTS in Spanish and Tamil and also demonstrate that the performance of the approach is comparable with the previous work done on English. All annotated datasets used for experimentation will be released at https://github.com/amazon-research/proteno.

Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

This presentation will talk about how LightSeq helps ByteDance's products and why it is so fast.

LightSeq: A High Performance Inference Library for Transformers

Can unsupervised machine translation be improved by other languages? This paper proposed to improve unsupervised neural machine translation with cross-lingual supervision, which utilizes supervision signals from high resource language pairs to improve the translation of zero-source languages. Specifically, for training En-Ro system without parallel corpus, we can leverage the corpus from En-Fr and En-De to collectively train the translation from one language into many languages under one model.

Cross-lingual Supervision Improves Unsupervised Neural Machine Translation

Most of the recent natural language processing (NLP) studies are based on the pretrain-finetuning approach (PFA). However for small and medium-sized industries with insufficient hardware, there are many limitations in servicing latest PFA based NLP application software, due to slow speed and insufficient memory. Since these approaches generally require large amounts of data, it is much more difficult to service with PFA especially for low-resource languages. We propose a new tokenization method, ONE-Piece, to address this limitation. ONE-Piece combines morphologically-aware subword tokenization and vocabulary communicating method, which has not been carefully considered before. Our proposed method can also be utilized without modifying the model structure. We experiment by applying ONE-Piece to Korean, a morphologically-rich and low-resource language. We revealed that ONE-Piece with vanilla transformer model can achieve comparable performance to the current Korean-English machine translation state-of-the-art model.

Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification

Automatic translation of dialogue texts is a much needed demand in many real life scenarios. However, current neural machine translation systems usually deliver unsatisfying translation results of dialogue texts. In this paper, we conduct a deep analysis of a dialogue corpus and summarize three major issues on dialogue translation. In response to these challenges, we propose a joint learning method to identify omission and typo in the process of translating and utilize context to translate dialogue utterances.

Autocorrect in the Process of Translation --- Multi-task Learning Improves Dialogue Machine Translation

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further finetuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.

Practical Transformer-based Multilingual Text Classification

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Practical Transformer-based Multilingual Text Classification

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Next from NAACL 2021

When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages

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HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

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