Thailand

In this overview article we describe an application designed to enable communication between health practitioners and patients who do not share a common language, in situations where professional interpreters are not available. Built on the principle of a fixed phrase translator, the application implements different natural language processing (NLP) technologies, such as speech recognition, neural machine translation and text-to-speech to improve usability. Its design allows easy portability to new domains and integration of different types of output for multiple target audiences. Even though BabelDr is far from solving the problem of miscommunication between patients and doctors, it is a clear example of NLP in a real world application designed to help minority groups to communicate in a medical context. It also gives some insights into the relevant criteria for the development of such an application.

ACL-IJCNLP 2021

A Speech-enabled Fixed-phrase Translator for Healthcare Accessibility

phraselator

minority languages

speech-to-text

speech-enabled phraselator

text-to-speech

accessibility

healthcare

backtranslation

natural language processing

ellipsis

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

Although neural models have achieved competitive results in dialogue systems, they have shown limited ability in representing core semantics, such as ignoring important entities.
To this end, we exploit Abstract Meaning Representation (AMR) to help dialogue modeling. 
Compared with the textual input, AMR explicitly provides core semantic knowledge and reduces data sparsity.
We develop an algorithm to construct dialogue-level AMR graphs from sentence-level AMRs and explore two ways to incorporate AMRs into dialogue systems.
Experimental results on both dialogue understanding and response generation tasks show the superiority of our model.
To our knowledge, we are the first to leverage a formal semantic representation into neural dialogue modeling.

Semantic Representation for Dialogue Modeling

We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on heterogeneous dialog corpora, a task-grounded response generation model, which can generate dialog responses grounded in user goals and real-world knowledge for task completion. The pre-trained model can be efficiently adapted to accomplish new tasks with a handful of task-specific dialogs via machine teaching, where training samples are generated by human teachers interacting with the system. Experiments show that (i) SOLOIST creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot fine-tuning settings, SOLOIST significantly outperforms existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost of fine-tuning. The pre-trained models and codes are available at https://aka.ms/soloist.

SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching

NeurST aims to facilitate NLP researchers to get started on end-to-end speech translation (ST) and build advanced neural machine translation (NMT) models. It has careful design with high extensibility and scalability. It provides step-by-step recipes for feature extraction, data preprocessing, distributed training, and evaluation. The performance of NeurST on several benchmark datasets is strong and reproducible, which can be regarded as reliable baselines for future research.

NeurST: Neural Speech Translation Toolkit

With more than 7000 languages worldwide, multilingual natural language rocessing (NLP) is essential both from an academic and commercial perspective. Researching 
typological properties of languages is fundamental for progress in multilingual NLP. Examples include assessing language similarity for effective transfer learning, 
injecting inductive biases into machine learning models or creating resources such as dictionaries and inflection tables. We provide ParCourE, an online tool that allows to browse a word-aligned parallel corpus, covering 1334 languages. We give evidence that this is useful for typological research. ParCourE can be set up for any
parallel corpus and can thus be used for typological research on other corpora as well as for exploring their quality and properties.

ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpus

We present MT-Telescope, a visualization platform designed to facilitate comparative analysis of the output quality of two Machine Translation (MT) systems. While automated MT evaluation metrics are commonly used to evaluate MT systems at a corpus level, our platform supports fine-grained segment-level analysis and interactive visualizations that expose the fundamental differences in the performance of the compared systems. MT-Telescope also supports dynamic corpus filtering to enable focused analysis on specific phenomena such as; translation of named entities, handling of terminology, and the impact of input segment length on translation quality. Furthermore, the platform provides a bootstrapped t-test for statistical significance as a means of evaluating the rigor of the resulting system ranking. MT-Telescope is open source, written in Python, and is built around a user-friendly and dynamic web interface. Complementing other existing tools, our platform is designed to facilitate and promote the broader adoption of more rigorous analysis practices in the evaluation of MT quality.

{MT}-{T}elescope: {A}n interactive platform for contrastive evaluation of {MT} systems

Health professional regulators aim to protect the health and well-being of patients and the public by setting standards for scrutinising and overseeing the training and conduct of health and care professionals. A major task of such regulators is the investigation of complaints against practitioners. However, processing a complaint often lasts several months and is particularly costly. Hence, we worked with international regulators from different countries(the UK, US and Australia), to develop the first decision support tool that aims to help such regulators process complaints more efficiently. Our system uses state-of-the-art machine learning and natural language processing techniques to process complaints and predict their risk level. Our tool also provides additional useful information including explanations, to help the regulatory staff interpret the prediction results, and similar past cases as well as non-compliance to regulations, to support the decision making.

Supporting Complaints Investigation for Nursing and Midwifery Regulatory Agencies

Tackling online hatred using informed textual responses -- called counter narratives -- has been brought under the spotlight recently. Accordingly, a research line has emerged to automatically generate counter narratives in order to facilitate the direct intervention in the hate discussion and to prevent hate content from further spreading. Still, current neural approaches tend to produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples. Moreover, these models can create plausible but not necessarily true arguments. In this paper we present the first complete knowledge-bound counter narrative generation pipeline, grounded in an external knowledge repository that can provide more informative content to fight online hatred. Together with our approach, we present a series of experiments that show its feasibility to produce suitable and informative counter narratives in in-domain and cross-domain settings.

Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech

We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for processing toxic content in social media. While much work has been done for the English language in this field, it has never been solved for the Russian language yet. We test two types of models -- unsupervised approach based on BERT architecture that performs local corrections and supervised approach based on pretrained language GPT-2 model -- and compare them with several baselines. In addition, we describe evaluation setup providing training datasets and metrics for automatic evaluation. The results show that the tested approaches can be successfully used for detoxification, although there is room for improvement.

Methods for Detoxification of Texts for the Russian Language

The range of works that can be considered as developing NLP for social good (NLP4SG) is enormous. While many of them target the identification of hate speech or fake news, there are others that address, e.g., text simplification to alleviate consequences of dyslexia, or coaching strategies to fight depression. However, so far, there is no clear picture of what areas are targeted by NLP4SG, who are the actors, which are the main scenarios and what are the topics that have been left aside. In order to obtain a clearer view in this respect, we first propose a working definition of NLP4SG and identify some primary aspects that are crucial for NLP4SG, including, e.g., tackled areas, ethics, privacy and bias. Then, we draw upon a corpus of around 50,000 articles downloaded from the ACL Anthology. Based on a list of keywords retrieved from the literature and revised in view of the task, we retrieve from this corpus articles that can be considered to be on NLP4SG according to our definition and analyze them. The result of the analysis is a map of the current NLP4SG research and insights concerning the white spots on this map.

Cartography of Natural Language Processing for Social Good (NLP4SG): Searching for Definitions, Statistics and White Spots

We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems. The adoption of PD methods by NLP will help to alleviate issues concerning the development of more democratic, fairer, less-biased technologies to process natural lan- guage data. This short paper is the outcome of an ongoing dialogue between designers and NLP experts and adopts a non-standard format following previous work by Traum (2000); Bender (2013); Abzianidze and Bos (2019). Every section is a guiding principle. While principles 1–3 illustrate assumptions and methods that inform community-based PD practices, we used two fictional design scenarios (Encinas and Blythe, 2018), which build on top of situations familiar to the authors, to elicit the identification of the other 6. Prin- ciples 4–6 describe the impact of PD methods on the design of NLP systems, targeting two critical aspects: data collection & annota- tion, and the deployment & evaluation. Finally, principles 7–9 guide a new reflexivity of the NLP research with respect to its context, ac- tors and participants, and aims. We hope this guide will offer inspiration and a road-map to develop a new generation of PD-inspired NLP.

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