United States

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.

NAACL 2021

Continuous Model Improvement for Language Understanding with Machine Translation

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

We propose HTCInfoMax for hierarchical text classification (HTC) by introducing information maximization which includes two modules: text-label mutual information maximization and label prior matching to address the limitations of previous SOTA model HiAGM.

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

Variational autoencoders (VAEs) are widely used for latent variable modeling of text. We focus on variations that learn expressive prior distributions over the latent variable. We find that existing training strategies are not effective for learning rich priors, so we add the importance-sampled log marginal likelihood as a second term to the standard VAE objective to help when learning the prior. Doing so improves results for all priors evaluated, including a novel choice for sentence VAEs based on normalizing flows (NF). Priors parameterized with NF are no longer constrained to a specific distribution family, allowing a more flexible way to encode the data distribution. Our model, which we call FlowPrior, shows a substantial improvement in language modeling tasks compared to strong baselines. We demonstrate that FlowPrior learns an expres- sive prior with analysis and several forms of evaluation involving generation.

FlowPrior: Learning Expressive Priors for Latent Variable Sentence Models

Deep Learning-based NLP systems can be sensitive to unseen tokens and hard to learn with high-dimensional inputs, which critically hinder learning generalization. We introduce an approach by grouping input words based on their semantic diversity to simplify input language representation with low ambiguity. Since the semantically diverse words reside in different contexts, we are able to substitute words with their groups and still distinguish word meanings relying on their contexts. We design several algorithms that compute diverse groupings based on random sampling, geometric distances, and entropy maximization, and we prove formal guarantees for the entropy-based algorithms. Experimental results show that our methods generalize NLP models and demonstrate enhanced accuracy on POS tagging and LM tasks and significant improvements on medium-scale machine translation tasks, up to +6.5 BLEU points.

Grouping Words with Semantic Diversity

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.

Modeling Content and Context with Deep Relational Learning

In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved. This may result in intent classification and slot tagging errors. In this work, we propose to leverage Entity Resolution (ER) features in NLU reranking and introduce a novel loss term based on ER signals to better learn model weights in the reranking framework. In addition, for a multi-domain dialog scenario, we propose a score distribution matching method to ensure scores generated by the NLU reranking models for different domains are properly calibrated. In offline experiments, we demonstrate our proposed approach significantly outperforms the baseline model on both single-domain and cross-domain evaluations.

Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems

We investigate NEL in multi-turn open-domain conversations. Considering the characteristic of dialogs, where the meaning of the current utterance often varies depending on the context, we design a context-aware Named Entity Recognition model and an Entity Resolution model. Experimental results on three datasets prove that using context information improves the entity recognition and resolution performance. Extrinsic evaluation on response generation also validates the effectiveness of the entity information.

Entity Resolution in Open-domain Conversations

One main challenge in building task-oriented dialogue systems is the limited amount of su- pervised training data available. In this work, we present a method for training retrieval- based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset. We show that pretraining using unlabeled data can bring better model performance with a 31% boost in Recall@1 compared with no pretraining. The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26% offline and 33% online performance im- provement in Recall@1 over the pretrained model. The model is deployed in an agent- support application and evaluated on live cus- tomer service contacts, providing additional insights into the real-world implications com- pared with most other publications in the do- main often using asynchronous transcripts (e.g. Reddit data). The high performance of 74% Recall@1 shown in the customer service ex- ample demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge.

Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting

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.

Continuous Model Improvement for Language Understanding with Machine Translation

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