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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.

ACL-IJCNLP 2021

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

nlp4sg

statistics

social good

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

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

workshop paper

Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks. In this paper, we probe the effectiveness of domain-adaptive pretraining objectives on downstream tasks. In particular, three objectives, including a novel objective focusing on modeling predicate-argument relations, are evaluated on two challenging dialogue understanding tasks. Experimental results demonstrate that domain-adaptive pretraining with proper objectives can significantly improve the performance of a strong baseline on these tasks, achieving the new state-of-the-art performances.

Domain-Adaptive Pretraining Methods for Dialogue Understanding

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

In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories of VAE, GAN, and pretrained language models. Meanwhile, our library maintains sufficient modularity and extensibility by properly decomposing the model architecture, inference, and learning process into highly reusable modules, which allows users to easily incorporate new models into our framework. The above features make TextBox specially suitable for researchers and practitioners to quickly reproduce baseline models and develop new models. TextBox is implemented based on PyTorch, and released under Apache License 2.0 at the link https: //github.com/RUCAIBox/TextBox.

TextBox: A Unified, Modularized, and Extensible Framework for Text Generation

ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website (https://ascent.mpi-inf.mpg.de/) and an introductory video (https://youtu.be/qMkJXqu_Yd4) are both available online.

Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering

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

Technologies for enhancing well-being, healthcare vigilance and monitoring are on the rise.
However, despite patient interest, such technologies suffer from low adoption. One hypothesis for this limited adoption is loss of human interaction that is central to doctor-patient encounters. In this paper we seek to address this limitation via a conversational agent that adopts one aspect of in-person doctor-patient interactions: A human avatar to facilitate medical grounded question answering. This is akin to the in-person scenario where the doctor may point to the human body or the patient may point to their own body to express their conditions. Additionally, our agent has multiple interaction modes, that may give more options for the patient to use the agent, not just for medical question answering, but also to engage in conversations about general topics and current events. Both the avatar, and the multiple interaction modes could help improve adherence.

We present a high level overview of the design of our agent, Marie Bot Wellbeing. We also report implementation details of our early prototype , and present preliminary results

A Grounded Well-being Conversational Agentwith Multiple Interaction Modes: Preliminary Results

Augmentative and Alternative Communication (AAC) devices and applications are intended to make it easier for individuals with complex communication needs to participate in conversations. However, these devices have low adoption and retention rates. We review prior work with text recommendation systems that have not been successful in mitigating these problems. To address these gaps, we propose applying Dialogue Act classification to AAC conversations. We evaluated the performance of a state of the art model on a limited AAC dataset that was trained on both AAC and non-AAC datasets. The one trained on AAC (accuracy = 38.6%) achieved better performance than that trained on a non-AAC corpus (accuracy = 34.1%). These results reflect the need to incorporate representative datasets in later experiments. We discuss the need to collect more labeled AAC datasets and propose areas of future work.


Dialogue Act Classification for Augmentative and Alternative Communication

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.

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Domain-Adaptive Pretraining Methods for Dialogue Understanding

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