yuz9yuz/NLPImpact huggingface dataset:
Title: Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Abstract: Word segmentation plays a pivotal role in improving any Arabic NLP application. Therefore, a lot of research has been spent in improving its accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii) domain/dialect dependent. We explore three language-independent alternatives to morphological segmentation using: i) data-driven sub-word units, ii) characters as a unit of learning, and iii) word embeddings learned using a character CNN (Convolution Neural Network). On the tasks of Machine Translation and POS tagging, we found these methods to achieve close to, and occasionally surpass state-of-the-art performance. In our analysis, we show that a neural machine translation system is sensitive to the ratio of source and target tokens, and a ratio close to 1 or greater, gives optimal performance.
Title: Fast and Accurate Neural Word Segmentation for Chinese
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Abstract: Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. In this paper, we propose a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.
Title: Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task
Venue: ACL (2017)
Topic: resources and evaluation
Abstract: We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7%. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5%. We then analyze this “ending-only” task setting. We estimate human accuracy to be 78% and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.
Title: Neural Semantic Parsing over Multiple Knowledge-bases
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Abstract: A fundamental challenge in developing semantic parsers is the paucity of strong supervision in the form of language utterances annotated with logical form. In this paper, we propose to exploit structural regularities in language in different domains, and train semantic parsers over multiple knowledge-bases (KBs), while sharing information across datasets. We find that we can substantially improve parsing accuracy by training a single sequence-to-sequence model over multiple KBs, when providing an encoding of the domain at decoding time. Our model achieves state-of-the-art performance on the Overnight dataset (containing eight domains), improves performance over a single KB baseline from 75.6% to 79.6%, while obtaining a 7x reduction in the number of model parameters.
Title: Representing Sentences as Low-Rank Subspaces
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: Sentences are important semantic units of natural language. A generic, distributional representation of sentences that can capture the latent semantics is beneficial to multiple downstream applications. We observe a simple geometry of sentences – the word representations of a given sentence (on average 10.23 words in all SemEval datasets with a standard deviation 4.84) roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this observation, we represent a sentence by the low-rank subspace spanned by its word vectors. Such an unsupervised representation is empirically validated via semantic textual similarity tasks on 19 different datasets, where it outperforms the sophisticated neural network models, including skip-thought vectors, by 15% on average.
Title: Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
Venue: ACL (2017)
Topic: summarization
Abstract: Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.
Title: Determining Whether and When People Participate in the Events They Tweet About
Venue: ACL (2017)
Topic: resources and evaluation
Abstract: This paper describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in the future. We present new annotations using 1,096 event mentions, and experimental results showing that the task is challenging.
Title: Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Abstract: Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events (Barthel et al., 2016). Fabricated stories in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda. We show that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models does not improve performance. Incorporating linguistic features improves classification results, however, social interaction features are most informative for finer-grained separation between four types of suspicious news posts.
Title: Recognizing Counterfactual Thinking in Social Media Texts
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Abstract: Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.
Title: Temporal Orientation of Tweets for Predicting Income of Users
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Abstract: Automatically estimating a user’s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.
Title: Character-Aware Neural Morphological Disambiguation
Venue: ACL (2017)
Topic: phonology, morphology, and word segmentation
Abstract: We develop a language-independent, deep learning-based approach to the task of morphological disambiguation. Guided by the intuition that the correct analysis should be “most similar” to the context, we propose dense representations for morphological analyses and surface context and a simple yet effective way of combining the two to perform disambiguation. Our approach improves on the language-dependent state of the art for two agglutinative languages (Turkish and Kazakh) and can be potentially applied to other morphologically complex languages.
Title: Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Abstract: We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters. The character composition model shows great improvement over the word-lookup model, especially for parsing agglutinative languages. These improvements are even better than using pre-trained word embeddings from extra data. On the SPMRL data sets, our system outperforms the previous best greedy parser (Ballesteros et. al, 2015) by a margin of 3% on average.
Title: How (not) to train a dependency parser: The curious case of jackknifing part-of-speech taggers
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Abstract: In dependency parsing, jackknifing taggers is indiscriminately used as a simple adaptation strategy. Here, we empirically evaluate when and how (not) to use jackknifing in parsing. On 26 languages, we reveal a preference that conflicts with, and surpasses the ubiquitous ten-folding. We show no clear benefits of tagging the training data in cross-lingual parsing.
Title: Computational Characterization of Mental States: A Natural Language Processing Approach
Venue: ACL (2017)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Title: Improving Distributed Representations of Tweets - Present and Future
Venue: ACL (2017)
Topic: resources and evaluation
Title: Bilingual Word Embeddings with Bucketed CNN for Parallel Sentence Extraction
Venue: ACL (2017)
Topic: information extraction
Title: nQuery - A Natural Language Statement to SQL Query Generator
Venue: ACL (2017)
Topic: generation
Title: V for Vocab: An Intelligent Flashcard Application
Venue: ACL (2017)
Topic: nlp applications
Title: Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions
Venue: ACL (2017)
Topic: machine learning for nlp
Title: Building a Non-Trivial Paraphrase Corpus Using Multiple Machine Translation Systems
Venue: ACL (2017)
Topic: information extraction
Title: Segmentation Guided Attention Networks for Visual Question Answering
Venue: ACL (2017)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: Text-based Speaker Identification on Multiparty Dialogues Using Multi-document Convolutional Neural Networks
Venue: ACL (2017)
Topic: speech processing and spoken language understanding
Title: Variation Autoencoder Based Network Representation Learning for Classification
Venue: ACL (2017)
Topic: low-resource methods for nlp
Title: Blind Phoneme Segmentation With Temporal Prediction Errors
Venue: ACL (2017)
Topic: phonology, morphology, and word segmentation
Title: Automatic Generation of Jokes in Hindi
Venue: ACL (2017)
Topic: generation
Title: Word Embedding for Response-To-Text Assessment of Evidence
Venue: ACL (2017)
Topic: interpretability and analysis of models for nlp
Title: Domain Specific Automatic Question Generation from Text
Venue: ACL (2017)
Topic: generation
Title: SoccEval: An Annotation Schema for Rating Soccer Players
Venue: ACL (2017)
Topic: nlp applications
Title: Accent Adaptation for the Air Traffic Control Domain
Venue: ACL (2017)
Topic: machine learning for nlp
Title: Generating Steganographic Text with LSTMs
Venue: ACL (2017)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: Predicting Depression for Japanese Blog Text
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Title: Fast Forward Through Opportunistic Incremental Meaning Representation Construction
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Modeling Situations in Neural Chat Bots
Venue: ACL (2017)
Topic: resources and evaluation
Title: An Empirical Study on End-to-End Sentence Modelling
Venue: ACL (2017)
Topic: nlp applications
Title: Varying Linguistic Purposes of Emoji in (Twitter) Context
Venue: ACL (2017)
Topic: information retrieval and text mining
Title: Negotiation of Antibiotic Treatment in Medical Consultations: A Corpus Based Study
Venue: ACL (2017)
Topic: nlp applications
Title: Annotating tense, mood and voice for English, French and German
Venue: ACL (2017)
Topic: sentiment analysis, stylistic analysis, and argument mining
Title: Automating Biomedical Evidence Synthesis: RobotReviewer
Venue: ACL (2017)
Topic: nlp applications
Title: Benben: A Chinese Intelligent Conversational Robot
Venue: ACL (2017)
Topic: dialogue and interactive systems
Title: End-to-End Non-Factoid Question Answering with an Interactive Visualization of Neural Attention Weights
Venue: ACL (2017)
Topic: machine learning for nlp
Title: ESTEEM: A Novel Framework for Qualitatively Evaluating and Visualizing Spatiotemporal Embeddings in Social Media
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Title: Exploring Diachronic Lexical Semantics with JeSemE
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Extended Named Entity Recognition API and Its Applications in Language Education
Venue: ACL (2017)
Topic: information extraction
Title: Hafez: an Interactive Poetry Generation System
Venue: ACL (2017)
Topic: nlp applications
Title: Interactive Visual Analysis of Transcribed Multi-Party Discourse
Venue: ACL (2017)
Topic: computational social science and cultural analytics
Title: Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences
Venue: ACL (2017)
Topic: nlp applications
Title: Olelo: A Question Answering Application for Biomedicine
Venue: ACL (2017)
Topic: nlp applications
Title: OpenNMT: Open-Source Toolkit for Neural Machine Translation
Venue: ACL (2017)
Topic: machine translation
Title: PyDial: A Multi-domain Statistical Dialogue System Toolkit
Venue: ACL (2017)
Topic: dialogue and interactive systems
Title: RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Title: Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ
Venue: ACL (2017)
Topic: resources and evaluation
Title: Semedico: A Comprehensive Semantic Search Engine for the Life Sciences
Venue: ACL (2017)
Topic: nlp applications
Title: SuperAgent: A Customer Service Chatbot for E-commerce Websites
Venue: ACL (2017)
Topic: dialogue and interactive systems
Title: Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph
Venue: ACL (2017)
Topic: interpretability and analysis of models for nlp
Title: UCCAApp: Web-application for Syntactic and Semantic Phrase-based Annotation
Venue: ACL (2017)
Topic: syntax: tagging, chunking and parsing
Title: WebChild 2.0 : Fine-Grained Commonsense Knowledge Distillation
Venue: ACL (2017)
Topic: resources and evaluation
Title: Zara Returns: Improved Personality Induction and Adaptation by an Empathetic Virtual Agent
Venue: ACL (2017)
Topic: multimodality and language grounding to vision, robotics and beyond
Title: Multimodal Machine Learning: Integrating Language, Vision and Speech
Venue: ACL (2017)
Topic: multimodality and language grounding to vision, robotics and beyond
Abstract: Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with image and video captioning projects, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities.

This tutorial builds upon a recent course taught at Carnegie Mellon University during the Spring 2016 semester (CMU course 11-777) and two tutorials presented at CVPR 2016 and ICMI 2016. The present tutorial will review fundamental concepts of machine learning and deep neural networks before describing the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The tutorial will also present state-of-the-art algorithms that were recently proposed to solve multimodal applications such as image captioning, video descriptions and visual question-answer. We will also discuss the current and upcoming challenges.
Title: Deep Learning for Semantic Composition
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: Learning representation to model the meaning of text has been a core problem in NLP. The last several years have seen extensive interests on distributional approaches, in which text spans of different granularities are encoded as vectors of numerical values. If properly learned, such representation has showed to achieve the state-of-the-art performance on a wide range of NLP problems.

In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural network-based modeling for semantic composition, which aims to learn distributed representation for different granularities of text, e.g., phrases, sentences, or even documents, from their sub-component meaning representation, e.g., word embedding.
Title: Deep Learning for Dialogue Systems
Venue: ACL (2017)
Topic: dialogue and interactive systems
Abstract: In the past decade, goal-oriented spoken dialogue systems have been the most prominent component in today's virtual personal assistants. The classic dialogue systems have rather complex and/or modular pipelines. The advance of deep learning technologies has recently risen the applications of neural models to dialogue modeling. However, how to successfully apply deep learning based approaches to a dialogue system is still challenging. Hence, this tutorial is designed to focus on an overview of the dialogue system development while describing most recent research for building dialogue systems and summarizing the challenges, in order to allow researchers to study the potential improvements of the state-of-the-art dialogue systems. The tutorial material is available at http://deepdialogue.miulab.tw.
Title: Beyond Words: Deep Learning for Multiword Expressions and Collocations
Venue: ACL (2017)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: Deep learning has recently shown much promise for NLP applications. Traditionally, in most NLP approaches, documents or sentences are represented by a sparse bag-of-words representation. There is now a lot of work which goes beyond this by adopting a distributed representation of words, by constructing a so-called ``neural embedding'' or vector space representation of each word or document. The aim of this tutorial is to go beyond the learning of word vectors and present methods for learning vector representations for Multiword Expressions and bilingual phrase pairs, all of which are useful for various NLP applications.

This tutorial aims to provide attendees with a clear notion of the linguistic and distributional characteristics of Multiword Expressions (MWEs), their relevance for the intersection of deep learning and natural language processing, what methods and resources are available to support their use, and what more could be done in the future. Our target audience are researchers and practitioners in machine learning, parsing (syntactic and semantic) and language technology, not necessarily experts in MWEs, who are interested in tasks that involve or could benefit from considering MWEs as a pervasive phenomenon in human language and communication.
Title: Tutorial: Making Better Use of the Crowd
Venue: ACL (2017)
Topic: nlp applications
Abstract: Over the last decade, crowdsourcing has been used to harness the power of human computation to solve tasks that are notoriously difficult to solve with computers alone, such as determining whether or not an image contains a tree, rating the relevance of a website, or verifying the phone number of a business. The natural language processing community was early to embrace crowdsourcing as a tool for quickly and inexpensively obtaining annotated data to train NLP systems. Once this data is collected, it can be handed off to algorithms that learn to perform basic NLP tasks such as translation or parsing. Usually this handoff is where interaction with the crowd ends. The crowd provides the data, but the ultimate goal is to eventually take humans out of the loop. Are there better ways to make use of the crowd?

In this tutorial, I will begin with a showcase of innovative uses of crowdsourcing that go beyond data collection and annotation. I will discuss applications to natural language processing and machine learning, hybrid intelligence or “human in the loop” AI systems that leverage the complementary strengths of humans and machines in order to achieve more than either could achieve alone, and large scale studies of human behavior online. I will then spend the majority of the tutorial diving into recent research aimed at understanding who crowdworkers are, how they behave, and what this should teach us about best practices for interacting with the crowd.