yuz9yuz/NLPImpact huggingface dataset:
Title: ClaimPortal: Integrated Monitoring, Searching, Checking, and Analytics of Factual Claims on Twitter
Venue: ACL (2019)
Topic: resources and evaluation
Abstract: We present ClaimPortal, a web-based platform for monitoring, searching, checking, and analyzing English factual claims on Twitter from the American political domain. We explain the architecture of ClaimPortal, its components and functions, and the user interface. While the last several years have witnessed a substantial growth in interests and efforts in the area of computational fact-checking, ClaimPortal is a novel infrastructure in that fact-checkers have largely skipped factual claims in tweets. It can be a highly powerful tool to both general web users and fact-checkers. It will also be an educational resource in helping cultivate a society that is less susceptible to falsehoods.
Title: Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Venue: ACL (2019)
Topic: nlp applications
Abstract: We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.
Title: Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Venue: ACL (2019)
Topic: interpretability and analysis of models for nlp
Abstract: Embeddings are a fundamental component of many modern machine learning and natural language processing models. Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models. In this paper, we introduce Parallax, a tool explicitly designed for this task. Parallax allows the user to use both state-of-the-art embedding analysis methods (PCA and t-SNE) and a simple yet effective task-oriented approach where users can explicitly define the axes of the projection through algebraic formulae. %consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging. In this approach, embeddings are projected into a semantically meaningful subspace, which enhances interpretability and allows for more fine-grained analysis. We demonstrate the power of the tool and the proposed methodology through a series of case studies and a user study.
Title: Flambé: A Customizable Framework for Machine Learning Experiments
Venue: ACL (2019)
Topic: language modeling
Abstract: Flambé is a machine learning experimentation framework built to accelerate the entire research life cycle. Flambé’s main objective is to provide a unified interface for prototyping models, running experiments containing complex pipelines, monitoring those experiments in real-time, reporting results, and deploying a final model for inference. Flambé achieves both flexibility and simplicity by allowing users to write custom code but instantly include that code as a component in a larger system which is represented by a concise configuration file format. We demonstrate the application of the framework through a cutting-edge multistage use case: fine-tuning and distillation of a state of the art pretrained language model used for text classification.
Title: A Modular Tool for Automatic Summarization
Venue: ACL (2019)
Topic: summarization
Abstract: This paper introduces the first fine-grained modular tool for automatic summarization. Open source and written in Java, it is designed to be as straightforward as possible for end-users. Its modular architecture is meant to ease its maintenance and the development and integration of new modules. We hope that it will ease the work of researchers in automatic summarization by providing a reliable baseline for future works as well as an easy way to evaluate methods on different corpora.
Title: TARGER: Neural Argument Mining at Your Fingertips
Venue: ACL (2019)
Topic: sentiment analysis, stylistic analysis, and argument mining
Abstract: We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus. The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user’s side. The open source code ensures portability to other domains and use cases.
Title: MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool
Venue: ACL (2019)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons. MoNoise reaches a new state-of-art performance for six out of seven of these datasets. Furthermore, we allow the user to tune the ‘aggressiveness’ of the normalization, and show how the model can be made more efficient with only a small loss in performance. The online demo can be found on: http://www.robvandergoot.com/monoise and the corresponding code on: https://bitbucket.org/robvanderg/monoise/
Title: Level-Up: Learning to Improve Proficiency Level of Essays
Venue: ACL (2019)
Topic: nlp applications
Abstract: We introduce a method for generating suggestions on a given sentence for improving the proficiency level. In our approach, the sentence is transformed into a sequence of grammatical elements aimed at providing suggestions of more advanced grammar elements based on originals. The method involves parsing the sentence, identifying grammatical elements, and ranking related elements to recommend a higher level of grammatical element. We present a prototype tutoring system, Level-Up, that applies the method to English learners’ essays in order to assist them in writing and reading. Evaluation on a set of essays shows that our method does assist user in writing.
Title: Learning to Link Grammar and Encyclopedic Information of Assist ESL Learners
Venue: ACL (2019)
Topic: multilinguality and language diversity
Abstract: We introduce a system aimed at improving and expanding second language learners’ English vocabulary. In addition to word definitions, we provide rich lexical information such as collocations and grammar patterns for target words. We present Linggle Booster that takes an article, identifies target vocabulary, provides lexical information, and generates a quiz on target words. Linggle Booster also links named-entity to corresponding Wikipedia pages. Evaluation on a set of target words shows that the method have reasonably good performance in terms of generating useful and information for learning vocabulary.
Title: Latent Structure Models for Natural Language Processing
Venue: ACL (2019)
Topic: interpretability and analysis of models for nlp
Abstract: Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines. They are appealing for two main reasons: they allow incorporating structural bias during training, leading to more accurate models; and they allow discovering hidden linguistic structure, which provides better interpretability. This tutorial will cover recent advances in discrete latent structure models. We discuss their motivation, potential, and limitations, then explore in detail three strategies for designing such models: gradient approximation, reinforcement learning, and end-to-end differentiable methods. We highlight connections among all these methods, enumerating their strengths and weaknesses. The models we present and analyze have been applied to a wide variety of NLP tasks, including sentiment analysis, natural language inference, language modeling, machine translation, and semantic parsing. Examples and evaluation will be covered throughout. After attending the tutorial, a practitioner will be better informed about which method is best suited for their problem.
Title: Graph-Based Meaning Representations: Design and Processing
Venue: ACL (2019)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on different flavors of semantic graphs and associated terminology; (c) survey common frameworks for graph-based meaning representation and available graph banks; and (d) offer a technical overview of a representative selection of different parsing approaches.
Title: Discourse Analysis and Its Applications
Venue: ACL (2019)
Topic: discourse and pragmatics
Abstract: Discourse processing is a suite of Natural Language Processing (NLP) tasks to uncover linguistic structures from texts at several levels, which can support many downstream applications. This involves identifying the topic structure, the coherence structure, the coreference structure, and the conversation structure for conversational discourse. Taken together, these structures can inform text summarization, machine translation, essay scoring, sentiment analysis, information extraction, question answering, and thread recovery. The tutorial starts with an overview of basic concepts in discourse analysis – monologue vs. conversation, synchronous vs. asynchronous conversation, and key linguistic structures in discourse analysis. We also give an overview of linguistic structures and corresponding discourse analysis tasks that discourse researchers are generally interested in, as well as key applications on which these discourse structures have an impact.
Title: Computational Analysis of Political Texts: Bridging Research Efforts Across Communities
Venue: ACL (2019)
Topic: computational social science and cultural analytics
Abstract: In the last twenty years, political scientists started adopting and developing natural language processing (NLP) methods more actively in order to exploit text as an additional source of data in their analyses. Over the last decade the usage of computational methods for analysis of political texts has drastically expanded in scope, allowing for a sustained growth of the text-as-data community in political science. In political science, NLP methods have been extensively used for a number of analyses types and tasks, including inferring policy position of actors from textual evidence, detecting topics in political texts, and analyzing stylistic aspects of political texts (e.g., assessing the role of language ambiguity in framing the political agenda). Just like in numerous other domains, much of the work on computational analysis of political texts has been enabled and facilitated by the development of resources such as, the topically coded electoral programmes (e.g., the Manifesto Corpus) or topically coded legislative texts (e.g., the Comparative Agenda Project). Political scientists created resources and used available NLP methods to process textual data largely in isolation from the NLP community. At the same time, NLP researchers addressed closely related tasks such as election prediction, ideology classification, and stance detection. In other words, these two communities have been largely agnostic of one another, with NLP researchers mostly unaware of interesting applications in political science and political scientists not applying cutting-edge NLP methodology to their problems. The main goal of this tutorial is to systematize and analyze the body of research work on political texts from both communities. We aim to provide a gentle, all-round introduction to methods and tasks related to computational analysis of political texts. Our vision is to bring the two research communities closer to each other and contribute to faster and more significant developments in this interdisciplinary research area.
Title: Wikipedia as a Resource for Text Analysis and Retrieval
Venue: ACL (2019)
Topic: information retrieval and text mining
Abstract: This tutorial examines the role of Wikipedia in tasks related to text analysis and retrieval. Text analysis tasks, which take advantage of Wikipedia, include coreference resolution, word sense and entity disambiguation and information extraction. In information retrieval, a better understanding of the structure and meaning of queries helps in matching queries against documents, clustering search results, answer and entity retrieval and retrieving knowledge panels for queries asking about popular entities.
Title: Deep Bayesian Natural Language Processing
Venue: ACL (2019)
Topic: language modeling
Abstract: This introductory tutorial addresses the advances in deep Bayesian learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, “deep learning” is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The “semantic structure” in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The “distribution function” in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network, long short-term memory, sequence-to-sequence model, variational auto-encoder, generative adversarial network, attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network and policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies and domain applications are presented to tackle different issues in deep Bayesian processing, learning and understanding. At last, we will point out a number of directions and outlooks for future studies.
Title: Unsupervised Cross-Lingual Representation Learning
Venue: ACL (2019)
Topic: multilinguality and language diversity
Abstract: In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations. After providing a brief history of supervised cross-lingual word representations, we focus on: 1) how to induce weakly-supervised and unsupervised cross-lingual word representations in truly resource-poor settings where bilingual supervision cannot be guaranteed; 2) critical examinations of different training conditions and requirements under which unsupervised algorithms can and cannot work effectively; 3) more robust methods for distant language pairs that can mitigate instability issues and low performance for distant language pairs; 4) how to comprehensively evaluate such representations; and 5) diverse applications that benefit from cross-lingual word representations (e.g., MT, dialogue, cross-lingual sequence labeling and structured prediction applications, cross-lingual IR).
Title: Advances in Argument Mining
Venue: ACL (2019)
Topic: sentiment analysis, stylistic analysis, and argument mining
Abstract: This course aims to introduce students to an exciting and dynamic area that has witnessed remarkable growth over the past 36 months. Argument mining builds on opinion mining, sentiment analysis and related to tasks to automatically extract not just *what* people think, but *why* they hold the opinions they do. From being largely beyond the state of the art barely five years ago, there are now many hundreds of papers on the topic, millions of dollars of commercial and research investment, and the 6th ACL workshop on the topic will be in Florence in 2019. The tutors have delivered tutorials on argument mining at ACL 2016, at IJCAI 2016 and at ESSLLI 2017; for ACL 2019, we have developed a tutorial that provides a synthesis of the major advances in the area over the past three years.
Title: Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective
Venue: ACL (2019)
Topic: information extraction
Abstract: In this tutorial, we wish to cover the foundational, methodological, and system development aspects of translating structured data (such as data in tabular form) and knowledge bases (such as knowledge graphs) into natural language. The attendees of the tutorial will be able to take away from this tutorial, (1) the basic ideas around how modern NLP and NLG techniques could be applied to describe and summarize textual data in format that is non-linguistic in nature or has some structure, and (2) a few interesting open-ended questions, which could lead to significant research contributions in future. The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. Various solutions, starting from traditional rule based/heuristic driven and modern data-driven and ultra-modern deep-neural style architectures will be discussed, followed by a brief discussion on evaluation and quality estimation. A significant portion of the tutorial will be dedicated towards unsupervised, scalable, and adaptable solutions, given that systems for such an important task will never naturally enjoy sustainable large scale domain independent labeled (parallel) data.
Title: Learning to Understand Child-directed and Adult-directed Speech
Venue: ACL (2020)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Abstract: Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signal itself, such as prosodic and phonemic variation. Human language acquisition research indicates that child-directed speech helps language learners. This study explores the effect of child-directed speech when learning to extract semantic information from speech directly. We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS). We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better. The results suggest that this is at least partially due to linguistic rather than acoustic properties of the two registers, as we see the same pattern when looking at models trained on acoustically comparable synthetic speech.
Title: Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts
Venue: ACL (2020)
Topic: computational social science and cultural analytics
Abstract: Accurately diagnosing depression is difficult– requiring time-intensive interviews, assessments, and analysis. Hence, automated methods that can assess linguistic patterns in these interviews could help psychiatric professionals make faster, more informed decisions about diagnosis. We propose JLPC, a model that analyzes interview transcripts to identify depression while jointly categorizing interview prompts into latent categories. This latent categorization allows the model to define high-level conversational contexts that influence patterns of language in depressed individuals. We show that the proposed model not only outperforms competitive baselines, but that its latent prompt categories provide psycholinguistic insights about depression.
Title: Coach: A Coarse-to-Fine Approach for Cross-domain Slot Filling
Venue: ACL (2020)
Topic: dialogue and interactive systems
Abstract: As an essential task in task-oriented dialog systems, slot filling requires extensive training data in a certain domain. However, such data are not always available. Hence, cross-domain slot filling has naturally arisen to cope with this data scarcity problem. In this paper, we propose a Coarse-to-fine approach (Coach) for cross-domain slot filling. Our model first learns the general pattern of slot entities by detecting whether the tokens are slot entities or not. It then predicts the specific types for the slot entities. In addition, we propose a template regularization approach to improve the adaptation robustness by regularizing the representation of utterances based on utterance templates. Experimental results show that our model significantly outperforms state-of-the-art approaches in slot filling. Furthermore, our model can also be applied to the cross-domain named entity recognition task, and it achieves better adaptation performance than other existing baselines. The code is available at https://github.com/zliucr/coach.
Title: Designing Precise and Robust Dialogue Response Evaluators
Venue: ACL (2020)
Topic: dialogue and interactive systems
Abstract: Automatic dialogue response evaluator has been proposed as an alternative to automated metrics and human evaluation. However, existing automatic evaluators achieve only moderate correlation with human judgement and they are not robust. In this work, we propose to build a reference-free evaluator and exploit the power of semi-supervised training and pretrained (masked) language models. Experimental results demonstrate that the proposed evaluator achieves a strong correlation (> 0.6) with human judgement and generalizes robustly to diverse responses and corpora. We open-source the code and data in https://github.com/ZHAOTING/dialog-processing.