pwc-archive/papers-with-abstracts huggingface dataset:
MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions
Authors: Chieh-Yang Huang, Tristan Labetoulle, Ting-Hao Huang, Yi-Pei Chen, Hung-Chen Chen, Vallari Srivastava, Lun-Wei Ku
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Emotion Classification, General Classification

We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.
HeidelPlace: An Extensible Framework for Geoparsing
Authors: Ludwig Richter, Johanna Gei{\ss}, Andreas Spitz, Michael Gertz
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9

Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.
SupWSD: A Flexible Toolkit for Supervised Word Sense Disambiguation
Authors: Pap, Simone rea, Aless Raganato, ro, Claudio Delli Bovi
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Word Sense Disambiguation

In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD). This toolkit includes the implementation of a state-of-the-art supervised WSD system, together with a Natural Language Processing pipeline for preprocessing and feature extraction. Our aim is to provide an easy-to-use tool for the research community, designed to be modular, fast and scalable for training and testing on large datasets. The source code of SupWSD is available at \url{http://github.com/SI3P/SupWSD}.
Interactive Abstractive Summarization for Event News Tweets
Authors: Ori Shapira, Hadar Ronen, Meni Adler, Yael Amsterdamer, Judit Bar-Ilan, Ido Dagan
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.
Interactive Visualization and Manipulation of Attention-based Neural Machine Translation
Authors: Jaesong Lee, Joong-Hwi Shin, Jun-Seok Kim
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, NMT, Translation

While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior. We present an interactive interface for visualizing and intervening behavior of NMT, specifically concentrating on the behavior of beam search mechanism and attention component. The tool (1) visualizes search tree and attention and (2) provides interface to adjust search tree and attention weight (manually or automatically) at real-time. We show the tool gives various methods to understand NMT.
Should Neural Network Architecture Reflect Linguistic Structure?
Authors: Chris Dyer
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Reinforcement Learning

I explore the hypothesis that conventional neural network models (e.g., recurrent neural networks) are incorrectly biased for making linguistically sensible generalizations when learning, and that a better class of models is based on architectures that reflect hierarchical structures for which considerable behavioral evidence exists. I focus on the problem of modeling and representing the meanings of sentences. On the generation front, I introduce recurrent neural network grammars (RNNGs), a joint, generative model of phrase-structure trees and sentences. RNNGs operate via a recursive syntactic process reminiscent of probabilistic context-free grammar generation, but decisions are parameterized using RNNs that condition on the entire (top-down, left-to-right) syntactic derivation history, thus relaxing context-free independence assumptions, while retaining a bias toward explaining decisions via {``}syntactically local{''} conditioning contexts. Experiments show that RNNGs obtain better results in generating language than models that don{'}t exploit linguistic structure. On the representation front, I explore unsupervised learning of syntactic structures based on distant semantic supervision using a reinforcement-learning algorithm. The learner seeks a syntactic structure that provides a compositional architecture that produces a good representation for a downstream semantic task. Although the inferred structures are quite different from traditional syntactic analyses, the performance on the downstream tasks surpasses that of systems that use sequential RNNs and tree-structured RNNs based on treebank dependencies. This is joint work with Adhi Kuncoro, Dani Yogatama, Miguel Ballesteros, Phil Blunsom, Ed Grefenstette, Wang Ling, and Noah A. Smith.
Rational Distortions of Learners' Linguistic Input
Authors: Naomi Feldman
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Language Acquisition

Language acquisition can be modeled as a statistical inference problem: children use sentences and sounds in their input to infer linguistic structure. However, in many cases, children learn from data whose statistical structure is distorted relative to the language they are learning. Such distortions can arise either in the input itself, or as a result of children{'}s immature strategies for encoding their input. This work examines several cases in which the statistical structure of children{'}s input differs from the language being learned. Analyses show that these distortions of the input can be accounted for with a statistical learning framework by carefully considering the inference problems that learners solve during language acquisition
Parsing for Grammatical Relations via Graph Merging
Authors: Weiwei Sun, Yantao Du, Xiaojun Wan
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8

This paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach. To deal with this problem, we propose graph merging, a new perspective, for building flexible dependency graphs: Constructing complex graphs via constructing simple subgraphs. We discuss two key problems in this perspective: (1) how to decompose a complex graph into simple subgraphs, and (2) how to combine subgraphs into a coherent complex graph. Experiments demonstrate the effectiveness of graph merging. Our parser reaches state-of-the-art performance and is significantly better than two transition-based parsers.
Leveraging Eventive Information for Better Metaphor Detection and Classification
Authors: I-Hsuan Chen, Yunfei Long, Qin Lu, Chu-Ren Huang
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Classification, Clustering, General Classification

Metaphor detection has been both challenging and rewarding in natural language processing applications. This study offers a new approach based on eventive information in detecting metaphors by leveraging the Chinese writing system, which is a culturally bound ontological system organized according to the basic concepts represented by radicals. As such, the information represented is available in all Chinese text without pre-processing. Since metaphor detection is another culturally based conceptual representation, we hypothesize that sub-textual information can facilitate the identification and classification of the types of metaphoric events denoted in Chinese text. We propose a set of syntactic conditions crucial to event structures to improve the model based on the classification of radical groups. With the proposed syntactic conditions, the model achieves a performance of 0.8859 in terms of F-scores, making 1.7{\%} of improvement than the same classifier with only Bag-of-word features. Results show that eventive information can improve the effectiveness of metaphor detection. Event information is rooted in every language, and thus this approach has a high potential to be applied to metaphor detection in other languages.
Collaborative Partitioning for Coreference Resolution
Authors: Olga Uryupina, Aless Moschitti, ro
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: coreference-resolution, Coreference Resolution

This paper presents a collaborative partitioning algorithm{---}a novel ensemble-based approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the ensemble{'}s outputs in terms of a task-specific similarity measure. Our approach assumes a loose integration of individual components of the ensemble and can therefore combine arbitrary coreference resolvers, regardless of their models. Our experiments on the CoNLL dataset show that collaborative partitioning yields results superior to those attained by the individual components, for ensembles of both strong and weak systems. Moreover, by applying the collaborative partitioning algorithm on top of three state-of-the-art resolvers, we obtain the best coreference performance reported so far in the literature (MELA v08 score of 64.47).
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
Authors: Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Marco A. Valenzuela-Esc{\'a}rcega, Peter Clark, Michael Hammond
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Answer Selection, Interpretable Machine Learning, Question Answering

For many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes learning this difficult and expensive. Here we propose an approach that uses answer ranking as distant supervision for learning how to select informative justifications, where justifications serve as inferential connections between the question and the correct answer while often containing little lexical overlap with either. We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection. Our approach is informed by a set of features designed to combine both learned representations and explicit features to capture the connection between questions, answers, and answer justifications. We show that with this end-to-end approach we are able to significantly improve upon a strong IR baseline in both justification ranking (+9{\%} rated highly relevant) and answer selection (+6{\%} P@1).
Learning What is Essential in Questions
Authors: Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Dan Roth
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Information Retrieval, Question Answering, Semantic Parsing, Sentence

Question answering (QA) systems are easily distracted by irrelevant or redundant words in questions, especially when faced with long or multi-sentence questions in difficult domains. This paper introduces and studies the notion of essential question terms with the goal of improving such QA solvers. We illustrate the importance of essential question terms by showing that humans{'} ability to answer questions drops significantly when essential terms are eliminated from questions.We then develop a classifier that reliably (90{\%} mean average precision) identifies and ranks essential terms in questions. Finally, we use the classifier to demonstrate that the notion of question term essentiality allows state-of-the-art QA solver for elementary-level science questions to make better and more informed decisions,improving performance by up to 5{\%}.We also introduce a new dataset of over 2,200 crowd-sourced essential terms annotated science questions.
Modeling Context Words as Regions: An Ordinal Regression Approach to Word Embedding
Authors: Shoaib Jameel, Steven Schockaert
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Diversity, regression, Word Embeddings

Vector representations of word meaning have found many applications in the field of natural language processing. Word vectors intuitively represent the average context in which a given word tends to occur, but they cannot explicitly model the diversity of these contexts. Although region representations of word meaning offer a natural alternative to word vectors, only few methods have been proposed that can effectively learn word regions. In this paper, we propose a new word embedding model which is based on SVM regression. We show that the underlying ranking interpretation of word contexts is sufficient to match, and sometimes outperform, the performance of popular methods such as Skip-gram. Furthermore, we show that by using a quadratic kernel, we can effectively learn word regions, which outperform existing unsupervised models for the task of hypernym detection.
An Artificial Language Evaluation of Distributional Semantic Models
Authors: Fatemeh Torabi Asr, Michael Jones
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Word Embeddings, Word Similarity

Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.
Learning Word Representations with Regularization from Prior Knowledge
Authors: Yan Song, Chia-Jung Lee, Fei Xia
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Language Modeling, Language Modelling, Learning Word Embeddings, Sentiment Analysis, Sentiment Classification, Word Embeddings, Word Similarity

Conventional word embeddings are trained with specific criteria (e.g., based on language modeling or co-occurrence) inside a single information source, disregarding the opportunity for further calibration using external knowledge. This paper presents a unified framework that leverages pre-learned or external priors, in the form of a regularizer, for enhancing conventional language model-based embedding learning. We consider two types of regularizers. The first type is derived from topic distribution by running LDA on unlabeled data. The second type is based on dictionaries that are created with human annotation efforts. To effectively learn with the regularizers, we propose a novel data structure, trajectory softmax, in this paper. The resulting embeddings are evaluated by word similarity and sentiment classification. Experimental results show that our learning framework with regularization from prior knowledge improves embedding quality across multiple datasets, compared to a diverse collection of baseline methods.
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring
Authors: Fei Dong, Yue Zhang, Jie Yang
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Feature Engineering, Machine Translation, Sentence, Sentiment Analysis

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.
Feature Selection as Causal Inference: Experiments with Text Classification
Authors: Michael J. Paul
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Causal Inference, Classification, Document Classification, Domain Adaptation, feature selection, General Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

This paper proposes a matching technique for learning causal associations between word features and class labels in document classification. The goal is to identify more meaningful and generalizable features than with only correlational approaches. Experiments with sentiment classification show that the proposed method identifies interpretable word associations with sentiment and improves classification performance in a majority of cases. The proposed feature selection method is particularly effective when applied to out-of-domain data.
A Joint Model for Semantic Sequences: Frames, Entities, Sentiments
Authors: Haoruo Peng, Snigdha Chaturvedi, Dan Roth
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Cloze Test, Discourse Parsing, Language Modeling, Language Modelling, Natural Language Understanding

Understanding stories {--} sequences of events {--} is a crucial yet challenging natural language understanding task. These events typically carry multiple aspects of semantics including actions, entities and emotions. Not only does each individual aspect contribute to the meaning of the story, so does the interaction among these aspects. Building on this intuition, we propose to jointly model important aspects of semantic knowledge {--} frames, entities and sentiments {--} via a semantic language model. We achieve this by first representing these aspects{'} semantic units at an appropriate level of abstraction and then using the resulting vector representations for each semantic aspect to learn a joint representation via a neural language model. We show that the joint semantic language model is of high quality and can generate better semantic sequences than models that operate on the word level. We further demonstrate that our joint model can be applied to story cloze test and shallow discourse parsing tasks with improved performance and that each semantic aspect contributes to the model.
Neural Sequence-to-sequence Learning of Internal Word Structure
Authors: Tatyana Ruzsics, Tanja Samard{\v{z}}i{\'c}
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

Learning internal word structure has recently been recognized as an important step in various multilingual processing tasks and in theoretical language comparison. In this paper, we present a neural encoder-decoder model for learning canonical morphological segmentation. Our model combines character-level sequence-to-sequence transformation with a language model over canonical segments. We obtain up to 4{\%} improvement over a strong character-level encoder-decoder baseline for three languages. Our model outperforms the previous state-of-the-art for two languages, while eliminating the need for external resources such as large dictionaries. Finally, by comparing the performance of encoder-decoder and classical statistical machine translation systems trained with and without corpus counts, we show that including corpus counts is beneficial to both approaches.
Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts
Authors: Henry Y. Chen, Ethan Zhou, Jinho D. Choi
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: coreference-resolution, Coreference Resolution, Entity Linking, Entity Resolution, Question Answering

This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76{\%} and the accuracy of 95.30{\%} for character identification.
Cross-language Learning with Adversarial Neural Networks
Authors: Shafiq Joty, Preslav Nakov, Llu{\'\i}s M{\`a}rquez, Israa Jaradat
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Community Question Answering, Domain Adaptation, Machine Translation, Question Answering, Question Similarity, Reranking, Word Embeddings

We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. In particular, we propose to use adversarial training of neural networks to learn high-level features that are discriminative for the main learning task, and at the same time are invariant across the input languages. The evaluation results show sizable improvements for our cross-language adversarial neural network (CLANN) model over a strong non-adversarial system.
Knowledge Tracing in Sequential Learning of Inflected Vocabulary
Authors: Adithya Renduchintala, Philipp Koehn, Jason Eisner
Date: 2017-08-01 00:00:00
Proceeding: CONLL 2017 8
Tasks: Knowledge Tracing, Structured Prediction

We present a feature-rich knowledge tracing method that captures a student{'}s acquisition and retention of knowledge during a foreign language phrase learning task. We model the student{'}s behavior as making predictions under a log-linear model, and adopt a neural gating mechanism to model how the student updates their log-linear parameters in response to feedback. The gating mechanism allows the model to learn complex patterns of retention and acquisition for each feature, while the log-linear parameterization results in an interpretable knowledge state. We collect human data and evaluate several versions of the model.