JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights

In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

Learning Latent Characteristics of Data and Models using Item Response Theory

A supervised machine learning model is trained with a large set of labeled training data, and evaluated on a smaller but still large set of test data. Especially with deep neural networks (DNNs), the complexity of the model requires that an extremely large data set is collected to prevent overfitting. It is often the case that these models do not take into account specific attributes of the training set examples, but instead treat each equally in the process of model training. This is due to the fact that it is difficult to model latent traits of individual examples at the scale of hundreds of thousands or millions of data points. However, there exist a set of psychometric methods that can model attributes of specific examples and can greatly improve model training and evaluation in the supervised learning process. Item Response Theory (IRT) is a well-studied psychometric methodology for scale construction and evaluation. IRT jointly models human ability and example characteristics such as difficulty based on human response data. We introduce new evaluation metrics for both humans and machine learning models build using IRT, and propose new methods for applying IRT to machine learning-scale data. We use IRT to make contributions to the machine learning community in the following areas: (i) new test sets for evaluating machine learning models with respect to a human population, (ii) new insights about how deep-learning models learn by tracking example difficulty and training conditions, and (iii) new methods for data selection and curriculum building to improve model training efficiency, (iv) a new test of electronic health literacy built with questions extracted from de-identified patient Electronic Health Records (EHRs). We first introduce two new evaluation sets built and validated using IRT. These tests are the first IRT test sets to be applied to natural language processing tasks. Using IRT test sets allows for more comprehensive comparison of NLP models. Second, by modeling the difficulty of test set examples, we identify patterns that emerge when training deep neural network models that are consistent with human learning patterns. Specifically, as models are trained with larger training sets, they learn easy test set examples more quickly than hard examples. Third, we present a method for using soft labels on a subset of training data to improve deep learning model generalization. We show that fine-tuning a trained deep neural network with as little as 0.1% of the training data can improve model generalization in terms of test set accuracy. Fourth, we propose a new method for estimating IRT example and model parameters that allows for learning parameters at a much larger scale than previously available to accommodate the large data sets required for deep learning. This allows for learning IRT models at machine learning scale, with hundreds of thousands of examples and large ensembles of machine learning models. The response patterns of machine learning models can be used to learn IRT example characteristics instead of human response patterns. Fifth, we introduce a dynamic curriculum learning process that estimates model competency during training to adaptively select training data that is appropriate for learning at the given epoch. Finally, we introduce the ComprehENotes test, the first test of EHR comprehension for humans. The test is an accurate measure for identifying individuals with low EHR note comprehension ability, and validates the effectiveness of previously self-reported patient comprehension evaluations

Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading

A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data. Subsequently, we leverage this structure to measure the intensity of these relationships. By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics. Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact.Comment: accepted at EMNLP 202

History Modeling for Conversational Information Retrieval

Conversational search is an embodiment of an iterative and interactive approach to information retrieval (IR) that has been studied for decades. Due to the recent rise of intelligent personal assistants, such as Siri, Alexa, AliMe, Cortana, and Google Assistant, a growing part of the population is moving their information-seeking activities to voice- or text-based conversational interfaces. One of the major challenges of conversational search is to leverage the conversation history to understand and fulfill the users\u27 information needs. In this dissertation work, we investigate history modeling approaches for conversational information retrieval. We start from history modeling for user intent prediction. We analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns, followed by a study of user intent prediction in an information-seeking setting with both feature-based methods and deep learning methods. We then move to history modeling for conversational question answering (ConvQA), which can be considered as a simplified setting of conversational search. We first propose a positional history answer embedding (PosHAE) method to seamlessly integrate conversation history into a ConvQA model based on BERT. We then build upon this method and design a history attention mechanism (HAM) to conduct a ``soft selection\u27\u27 for conversation history. After this, we extend the previous ConvQA task to an open-retrieval (ORConvQA) setting to emphasize the fundamental role of retrieval in conversational search. In this setting, we learn to retrieve evidence from a large collection before extracting answers. We build an end-to-end system for ORConvQA, featuring a learnable dense retriever. We conduct experiments with both fully-supervised and weakly-supervised approaches to tackle the training challenges of ORConvQA. Finally, we study history modeling for conversational re-ranking. Given a history of user feedback behaviors, such as issuing a query, clicking a document, and skipping a document, we propose to introduce behavior awareness to a neural ranker. Our experimental results show that the history modeling approaches proposed in this dissertation can effectively improve the performance of different conversation tasks and provide new insights into conversational information retrieval

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Cross-Platform Question Answering in Social Networking Services

The last two decades have made the Internet a major source for knowledge seeking. Several platforms have been developed to find answers to one's questions such as search engines and online encyclopedias. The wide adoption of social networking services has pushed the possibilities even further by giving people the opportunity to stimulate the generation of answers that are not already present on the Internet. Some of these social media services are primarily community question answering (CQA) sites, while the others have a more general audience but can also be used to ask and answer questions. The choice of a particular platform (e.g., a CQA site, a microblogging service, or a search engine) by some user depends on several factors such as awareness of available resources and expectations from different platforms, and thus will sometimes be suboptimal. Hence, we introduce \emph{cross-platform question answering}, a framework that aims to improve our ability to satisfy complex information needs by returning answers from different platforms, including those where the question has not been originally asked. We propose to build this core capability by defining a general architecture for designing and implementing real-time services for answering naturally occurring questions. This architecture consists of four key components: (1) real-time detection of questions, (2) a set of platforms from which answers can be returned, (3) question processing by the selected answering systems, which optionally involves question transformation when questions are answered by services that enforce differing conventions from the original source, and (4) answer presentation, including ranking, merging, and deciding whether to return the answer. We demonstrate the feasibility of this general architecture by instantiating a restricted development version in which we collect the questions from one CQA website, one microblogging service or directly from the asker, and find answers from among some subset of those CQA and microblogging services. To enable the integration of new answering platforms in our architecture, we introduce a framework for automatic evaluation of their effectiveness