pwc-archive/papers-with-abstracts huggingface dataset:
OPUS-MT – Building open translation services for the World
Authors: Jörg Tiedemann, Santhosh Thottingal
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: Machine Translation, Translation

This paper presents OPUS-MT a project that focuses on the development of free resources and tools for machine translation. The current status is a repository of over 1,000 pre-trained neural machine translation models that are ready to be launched in on-line translation services. For this we also provide open source implementations of web applications that can run efficiently on average desktop hardware with a straightforward setup and installation.
OCR, Classification & Machine Translation (OCCAM)
Authors: Joachim Van den Bogaert, Arne Defauw, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch, Pavel Smrž, Michal Hradiš
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: Classification, image-classification, Image Classification, Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), Translation

The OCCAM project (Optical Character recognition, ClassificAtion & Machine Translation) aims at integrating the CEF (Connecting Europe Facility) Automated Translation service with image classification, Translation Memories (TMs), Optical Character Recognition (OCR), and Machine Translation (MT). It will support the automated translation of scanned business documents (a document format that, currently, cannot be processed by the CEF eTranslation service) and will also lead to a tool useful for the Digital Humanities domain.
CEFAT4Cities, a Natural Language Layer for the ISA2 Core Public Service Vocabulary
Authors: Joachim Van den Bogaert, Arne Defauw, Sara Szoc, Frederic Everaert, Koen Van Winckel, Alina Kramchaninova, Anna Bardadym, Tom Vanallemeersch
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: Translation

The CEFAT4Cities project (2020-2022) will create a “Smart Cities natural language context” (a software layer that facilitates the conversion of natural-language administrative procedures, into machine-readable data sets) on top of the existing ISA2 interoperability layer for public services. Integration with the FIWARE/ORION “Smart City” Context Broker, will make existing, paper-based, public services discoverable through “Smart City” frameworks, thus allowing for the development of more sophisticated and more user-friendly public services applications. An automated translation component will be included, to provide a solution that can be used by all EU Member States. As a result, the project will allow EU citizens and businesses to interact with public services on the city, national, regional and EU level, in their own language.
Assessing the Comprehensibility of Automatic Translations (ArisToCAT)
Authors: Lieve Macken, Margot Fonteyne, Arda Tezcan, Joke Daems
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11

The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output. In this project description, we summarize the main results of the project and present future work.
Let MT simplify and speed up your Alignment for TM creation
Authors: Judith Klein, Giorgio Bernardinello
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11

Large quantities of multilingual legal documents are waiting to be regularly aligned and used for future translations. For reasons of time, effort and cost, manual alignment is not an option. Automatically aligned segments are suitable for concordance search but are unreliable for fuzzy search and pretranslation. MT-based alignment could be the key to improving the results.
An Overview of the SEBAMAT Project
Authors: Reinhard Rapp, George Tambouratzis
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: Machine Translation, Translation

SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT). Current MT systems typically take the semantics of a text only in so far into account as they are implicit in the underlying text corpora or dictionaries. Occasionally it has been argued that it may be difficult to advance MT quality to the next level as long as the systems do not make more explicit use of semantic knowledge. SEBAMAT aims to evaluate three approaches incorporating such knowledge into MT.
DeepSPIN: Deep Structured Prediction for Natural Language Processing
Authors: André F. T. Martins
Date: 2022-06-01 00:00:00
Proceeding: EAMT 2022 6
Tasks: Machine Translation, Prediction, Structured Prediction, Translation

DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation applications.
Project MAIA: Multilingual AI Agent Assistant
Authors: André F. T. Martins, Joao Graca, Paulo Dimas, Helena Moniz, Graham Neubig
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: AI Agent, BIG-bench Machine Learning, Translation

This paper presents the Multilingual Artificial Intelligence Agent Assistant (MAIA), a project led by Unbabel with the collaboration of CMU, INESC-ID and IT Lisbon. MAIA will employ cutting-edge machine learning and natural language processing technologies to build multilingual AI agent assistants, eliminating language barriers. MAIA’s translation layer will empower human agents to provide customer support in real-time, in any language, with human quality.
MTrill project: Machine Translation impact on language learning
Authors: Natália Resende, Andy Way
Date: 2020-11-01 00:00:00
Proceeding: EAMT 2020 11
Tasks: Language Acquisition, Machine Translation, Sentence, Translation

Over the last decades, massive research investments have been made in the development of machine translation (MT) systems (Gupta and Dhawan, 2019). This has brought about a paradigm shift in the performance of these language tools, leading to widespread use of popular MT systems (Gaspari and Hutchins, 2007). Although the first MT engines were used for gisting purposes, in recent years, there has been an increasing interest in using MT tools, especially the freely available online MT tools, for language teaching and learning (Clifford et al., 2013). The literature on MT and Computer Assisted Language Learning (CALL) shows that, over the years, MT systems have been facilitating language teaching and also language learning (Nin ̃o, 2006). It has been shown that MT tools can increase awareness of grammatical linguistic features of a foreign language. Research also shows the positive role of MT systems in the development of writing skills in English as well as in improving communication skills in English(Garcia and Pena, 2011). However, to date, the cognitive impact of MT on language acquisition and on the syntactic aspects of language processing has not yet been investigated and deserves further scrutiny. The MTril project aims at filling this gap in the literature by examining whether MT is contributing to a central aspect of language acquisition: the so-called language binding, i.e., the ability to combine single words properly in a grammatical sentence (Heyselaar et al., 2017; Ferreira and Bock, 2006). The project focus on the initial stages (pre-intermediate and intermediate) of the acquisition of English syntax by Brazilian Portuguese native speakers using MT systems as a support for language learning.
Zero-Shot Clinical Questionnaire Filling From Human-Machine Interactions
Authors: Farnaz Ghassemi Toudeshki, Philippe Jolivet, Alexandre Durand-Salmon, Anna Liednikova
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Question Answering, text-classification, Text Classification

In clinical studies, chatbots mimicking doctor-patient interactions are used for collecting information about the patient’s health state. Later, this information needs to be processed and structured for the doctor. One way to organize it is by automatically filling the questionnaires from the human-bot conversation. It would help the doctor to spot the possible issues. Since there is no such dataset available for this task and its collection is costly and sensitive, we explore the capacities of state-of-the-art zero-shot models for question answering, textual inference, and text classification. We provide a detailed analysis of the results and propose further directions for clinical questionnaire filling.
Can Question Generation Debias Question Answering Models? A Case Study on Question–Context Lexical Overlap
Authors: Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Data Augmentation, Question Answering, Question Generation, Question-Generation, Reading Comprehension

Question answering (QA) models for reading comprehension have been demonstrated to exploit unintended dataset biases such as question–context lexical overlap. This hinders QA models from generalizing to under-represented samples such as questions with low lexical overlap. Question generation (QG), a method for augmenting QA datasets, can be a solution for such performance degradation if QG can properly debias QA datasets. However, we discover that recent neural QG models are biased towards generating questions with high lexical overlap, which can amplify the dataset bias. Moreover, our analysis reveals that data augmentation with these QG models frequently impairs the performance on questions with low lexical overlap, while improving that on questions with high lexical overlap. To address this problem, we use a synonym replacement-based approach to augment questions with low lexical overlap. We demonstrate that the proposed data augmentation approach is simple yet effective to mitigate the degradation problem with only 70k synthetic examples.
What Can a Generative Language Model Answer About a Passage?
Authors: Douglas Summers-Stay, Claire Bonial, Clare Voss
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Language Modeling, Language Modelling

Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair. However, accuracy rates vary depending on the type of question asked. In this paper we keep the passage fixed, and test with a wide variety of question types, exploring the strengths and weaknesses of the GPT-3 language model. We provide the passage and test questions as a challenge set for other language models.
Eliciting Bias in Question Answering Models through Ambiguity
Authors: Andrew Mao, Naveen Raman, Matthew Shu, Eric Li, Franklin Yang, Jordan Boyd-Graber
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Articles, Question Answering

Question answering (QA) models use retriever and reader systems to answer questions. Reliance on training data by QA systems can amplify or reflect inequity through their responses. Many QA models, such as those for the SQuAD dataset, are trained and tested on a subset of Wikipedia articles which encode their own biases and also reproduce real-world inequality. Understanding how training data affects bias in QA systems can inform methods to mitigate inequity. We develop two sets of questions for closed and open domain questions respectively, which use ambiguous questions to probe QA models for bias. We feed three deep-learning-based QA systems with our question sets and evaluate responses for bias via the metrics. Using our metrics, we find that open-domain QA models amplify biases more than their closed-domain counterparts and propose that biases in the retriever surface more readily due to greater freedom of choice.
GANDALF: a General Character Name Description Dataset for Long Fiction
Authors: Fredrik Carlsson, Magnus Sahlgren, Fredrik Olsson, Amaru Cuba Gyllensten
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (MRQA) 2021 11
Tasks: Multiple-choice, Question Answering

This paper introduces a long-range multiple-choice Question Answering (QA) dataset, based on full-length fiction book texts. The questions are formulated as 10-way multiple-choice questions, where the task is to select the correct character name given a character description, or vice-versa. Each character description is formulated in natural text and often contains information from several sections throughout the book. We provide 20,000 questions created from 10,000 manually annotated descriptions of characters from 177 books containing 152,917 words on average. We address the current discourse regarding dataset bias and leakage by a simple anonymization procedure, which in turn enables interesting probing possibilities. Finally, we show that suitable baseline algorithms perform very poorly on this task, with the book size itself making it non-trivial to attempt a Transformer-based QA solution. This leaves ample room for future improvement, and hints at the need for a completely different type of solution.
Using Hierarchical Class Structure to Improve Fine-Grained Claim Classification
Authors: Erenay Dayanik, Andre Blessing, Nico Blokker, Sebastian Haunss, Jonas Kuhn, Gabriella Lapesa, Sebastian Padó
Date: 2021-08-01 00:00:00
Proceeding: ACL (spnlp) 2021 8
Tasks: Classification

The analysis of public debates crucially requires the classification of political demands according to hierarchical claim ontologies (e.g. for immigration, a supercategory “Controlling Migration” might have subcategories “Asylum limit” or “Border installations”). A major challenge for automatic claim classification is the large number and low frequency of such subclasses. We address it by jointly predicting pairs of matching super- and subcategories. We operationalize this idea by (a) encoding soft constraints in the claim classifier and (b) imposing hard constraints via Integer Linear Programming. Our experiments with different claim classifiers on a German immigration newspaper corpus show consistent performance increases for joint prediction, in particular for infrequent categories and discuss the complementarity of the two approaches.
Comparing Span Extraction Methods for Semantic Role Labeling
Authors: Zhisong Zhang, Emma Strubell, Eduard Hovy
Date: 2021-08-01 00:00:00
Proceeding: ACL (spnlp) 2021 8
Tasks: Semantic Role Labeling

In this work, we empirically compare span extraction methods for the task of semantic role labeling (SRL). While recent progress incorporating pre-trained contextualized representations into neural encoders has greatly improved SRL F1 performance on popular benchmarks, the potential costs and benefits of structured decoding in these models have become less clear. With extensive experiments on PropBank SRL datasets, we find that more structured decoding methods outperform BIO-tagging when using static (word type) embeddings across all experimental settings. However, when used in conjunction with pre-trained contextualized word representations, the benefits are diminished. We also experiment in cross-genre and cross-lingual settings and find similar trends. We further perform speed comparisons and provide analysis on the accuracy-efficiency trade-offs among different decoding methods.
Automatically explaining health information
Authors: Emiel Khramer
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11

Modern AI systems automatically learn from data using sophisticated statistical models. Explaining how these systems work and how they make their predictions therefore increasingly involves producing descriptions of how different probabilities are weighted and which uncertainties underlie these numbers. But what is the best way to (automatically) present such probabilistic explanations, do people actually understand them, and what is the potential impact of such information on people’s wellbeing? In this talk, I adress these questions in the context of systems that automatically generate personalised health information. The emergence of large national health registeries, such as the Dutch cancer registry, now make it possible to automatically generate descriptions of treatment options for new cancer patients based on data of comparable patients, including health and quality of life predictions following different treatments. I describe a series of studies, in which our team has investigated to what extent this information is currently provided to people, and under which conditions people actually want to have access to these kind of data-driven explanations. Additionally, we have studied whether there are different profiles in information needs, and what the best way is to provide probabilistic information and the associated undertainties to people.
Bias in AI-systems: A multi-step approach
Authors: Eirini Ntoutsi
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11
Tasks: Autonomous Driving, Decision Making, Fairness

Algorithmic-based decision making powered via AI and (big) data has already penetrated into almost all spheres of human life, from content recommendation and healthcare to predictive policing and autonomous driving, deeply affecting everyone, anywhere, anytime. While technology allows previously unthinkable optimizations in the automation of expensive human decision making, the risks that the technology can pose are also high, leading to an ever increasing public concern about the impact of the technology in our lives. The area of responsible AI has recently emerged in an attempt to put humans at the center of AI-based systems by considering aspects, such as fairness, reliability and privacy of decision-making systems. In this talk, we will focus on the fairness aspect. We will start with understanding the many sources of bias and how biases can enter at each step of the learning process and even get propagated/amplified from previous steps. We will continue with methods for mitigating bias which typically focus on some step of the pipeline (data, algorithms or results) and why it is important to target bias in each step and collectively, in the whole (machine) learning pipeline. We will conclude this talk by discussing accountability issues in connection to bias and in particular, proactive consideration via bias-aware data collection, processing and algorithmic selection and retroactive consideration via explanations.
Content Selection for Explanation Requests in Customer-Care Domain
Authors: Luca Anselma, Mirko Di Lascio, Dario Mana, Alessandro Mazzei, Manuela Sanguinetti
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11

This paper describes a content selection module for the generation of explanations in a dialogue system designed for customer care domain. First we describe the construction of a corpus of a dialogues containing explanation requests from customers to a virtual agent of a telco, and second we study and formalize the importance of a specific information content for the generated message. In particular, we adapt the notions of importance and relevance in the case of schematic knowledge bases.
ExTRA: Explainable Therapy-Related Annotations
Authors: Mat Rawsthorne, Tahseen Jilani, Jacob Andrews, Yunfei Long, Jeremie Clos, Samuel Malins, Daniel Hunt
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

In this paper we report progress on a novel explainable artificial intelligence (XAI) initiative applying Natural Language Processing (NLP) with elements of codesign to develop a text classifier for application in psychotherapy training. The task is to produce a tool that will facilitate therapists to review their sessions by automatically labelling transcript text with levels of interaction for patient activation in known psychological processes, using XAI to increase their trust in the model’s suggestions and client trajectory predictions. After pre-processing of the language features extracted from professionally annotated therapy session transcripts, we apply a supervised machine learning approach (CHAID) to classify interaction labels (negative, neutral, positive). Weighted samples are used to overcome class imbalanced data. The results show this initial model can make useful distinctions among the three labels of patient activation with 74% accuracy and provide insight into its reasoning. This ongoing project will additionally evaluate which XAI approaches can be used to increase the transparency of the tool to end users, exploring whether direct involvement of stakeholders improves usability of the XAI interface and therefore trust in the solution.
The Natural Language Pipeline, Neural Text Generation and Explainability
Authors: Juliette Faille, Albert Gatt, Claire Gardent
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11
Tasks: Data-to-Text Generation, Decoder, Survey, Text Generation

End-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into sub-modules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for breaking up the end-to-end encoder-decoder. We survey recent papers that integrate traditional NLG submodules in neural approaches and analyse their explainability. Our survey is a first step towards building explainable neural NLG models.
Towards Harnessing Natural Language Generation to Explain Black-box Models
Authors: Ettore Mariotti, Jose M. Alonso, Albert Gatt
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Text Generation

The opaque nature of many machine learning techniques prevents the wide adoption of powerful information processing tools for high stakes scenarios. The emerging field eXplainable Artificial Intelligence (XAI) aims at providing justifications for automatic decision-making systems in order to ensure reliability and trustworthiness in the users. For achieving this vision, we emphasize the importance of a natural language textual modality as a key component for a future intelligent interactive agent. We outline the challenges of XAI and review a set of publications that work in this direction.
Explaining Bayesian Networks in Natural Language: State of the Art and Challenges
Authors: Conor Hennessy, Alberto Bugarín, Ehud Reiter
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11
Tasks: Decision Making

In order to increase trust in the usage of Bayesian Networks and to cement their role as a model which can aid in critical decision making, the challenge of explainability must be faced. Previous attempts at explaining Bayesian Networks have largely focused on graphical or visual aids. In this paper we aim to highlight the importance of a natural language approach to explanation and to discuss some of the previous and state of the art attempts of the textual explanation of Bayesian Networks. We outline several challenges that remain to be addressed in the generation and validation of natural language explanations of Bayesian Networks. This can serve as a reference for future work on natural language explanations of Bayesian Networks.
Explaining data using causal Bayesian networks
Authors: Jaime Sevilla
Date: 2020-11-01 00:00:00
Proceeding: ACL (NL4XAI, INLG) 2020 11

I introduce Causal Bayesian Networks as a formalism for representing and explaining probabilistic causal relations, review the state of the art on learning Causal Bayesian Networks and suggest and illustrate a research avenue for studying pairwise identification of causal relations inspired by graphical causality criteria.