pwc-archive/papers-with-abstracts huggingface dataset:
Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling
Authors: Ryo Masumura, Tomohiro Tanaka, Ryuichiro Higashinaka, Hirokazu Masataki, Yushi Aono
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Classification, Feature Engineering, General Classification, Spoken Dialogue Systems, Transfer Learning

This paper is an initial study on multi-task and multi-lingual joint learning for lexical utterance classification. A major problem in constructing lexical utterance classification modules for spoken dialogue systems is that individual data resources are often limited or unbalanced among tasks and/or languages. Various studies have examined joint learning using neural-network based shared modeling; however, previous joint learning studies focused on either cross-task or cross-lingual knowledge transfer. In order to simultaneously support both multi-task and multi-lingual joint learning, our idea is to explicitly divide state-of-the-art neural lexical utterance classification into language-specific components that can be shared between different tasks and task-specific components that can be shared between different languages. In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets. We demonstrate the effectiveness of proposed method using Japanese and English data sets with three different lexical utterance classification tasks.
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
Authors: He Bai, Yu Zhou, Jiajun Zhang, Liang Zhao, Mei-Yuh Hwang, Cheng-qing Zong
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Cultural Vocal Bursts Intensity Prediction, domain classification, General Classification, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, slot-filling, Slot Filling, Spoken Language Understanding

To deploy a spoken language understanding (SLU) model to a new language, language transferring is desired to avoid the trouble of acquiring and labeling a new big SLU corpus. An SLU corpus is a monolingual corpus with domain/intent/slot labels. Translating the original SLU corpus into the target language is an attractive strategy. However, SLU corpora consist of plenty of semantic labels (slots), which general-purpose translators cannot handle well, not to mention additional culture differences. This paper focuses on the language transferring task given a small in-domain parallel SLU corpus. The in-domain parallel corpus can be used as the first adaptation on the general translator. But more importantly, we show how to use reinforcement learning (RL) to further adapt the adapted translator, where translated sentences with more proper slot tags receive higher rewards. Our reward is derived from the source input sentence exclusively, unlike reward via actor-critical methods or computing reward with a ground truth target sentence. Hence we can adapt the translator the second time, using the big monolingual SLU corpus from the source language. We evaluate our approach on Chinese to English language transferring for SLU systems. The experimental results show that the generated English SLU corpus via adaptation and reinforcement learning gives us over 97{\%} in the slot F1 score and over 84{\%} accuracy in domain classification. It demonstrates the effectiveness of the proposed language transferring method. Compared with naive translation, our proposed method improves domain classification accuracy by relatively 22{\%}, and the slot filling F1 score by relatively more than 71{\%}.
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation
Authors: Zongsheng Wang, Yunzhi Bai, Bowen Wu, Zhen Xu, Zhuoran Wang, Baoxun Wang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Diversity, Machine Translation, Response Generation

Generative dialog models usually adopt beam search as the inference method to generate responses. However, small-width beam search only focuses on the limited current optima. This deficiency named as myopic bias ultimately suppresses the diversity and probability of generated responses. Although increasing the beam width mitigates the myopic bias, it also proportionally slows down the inference efficiency. To alleviate the myopic bias in small-width beam search, this paper proposes a Prospective-Performance Network (PPN) to predict the future reward of the given partially-generated response, and the future reward is defined by the expectation of the partial response appearing in the top-ranked responses given by a larger-width beam search. Enhanced by PPN, the decoder can promote the results with great potential during the beam search phase. The experimental results on both Chinese and English corpora show that our method is promising to increase the quality and diversity of generated responses, with inference efficiency well maintained.
Adaptive Multi-Task Transfer Learning for Chinese Word Segmentation in Medical Text
Authors: Junjie Xing, Kenny Zhu, Shaodian Zhang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Chinese Word Segmentation, Transfer Learning

Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop when dealing with domain text, especially for a domain with lots of special terms and diverse writing styles, such as the biomedical domain. However, building domain-specific CWS requires extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant knowledge from high resource to low resource domains. Extensive experiments show that our model achieves consistently higher accuracy than the single-task CWS and other transfer learning baselines, especially when there is a large disparity between source and target domains.
Graph Based Decoding for Event Sequencing and Coreference Resolution
Authors: Zhengzhong Liu, Teruko Mitamura, Eduard Hovy
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: coreference-resolution, Coreference Resolution

Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.
DIDEC: The Dutch Image Description and Eye-tracking Corpus
Authors: Emiel van Miltenburg, {\'A}kos K{\'a}d{\'a}r, Ruud Koolen, Emiel Krahmer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Image Description, Specificity, Task 2

We present a corpus of spoken Dutch image descriptions, paired with two sets of eye-tracking data: Free viewing, where participants look at images without any particular purpose, and Description viewing, where we track eye movements while participants produce spoken descriptions of the images they are viewing. This paper describes the data collection procedure and the corpus itself, and provides an initial analysis of self-corrections in image descriptions. We also present two studies showing the potential of this data. Though these studies mainly serve as an example, we do find two interesting results: (1) the eye-tracking data for the description viewing task is more coherent than for the free-viewing task; (2) variation in image descriptions (also called {`}image specificity{'}; Jas and Parikh, 2015) is only moderately correlated across different languages. Our corpus can be used to gain a deeper understanding of the image description task, particularly how visual attention is correlated with the image description process.
Narrative Schema Stability in News Text
Authors: Dan Simonson, Anthony Davis
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Topic Models

We investigate the stability of narrative schemas (Chambers and Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson and Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of {``}more of the same.{''}
NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager
Authors: Idris Yusupov, Yurii Kuratov
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Goal-Oriented Dialog, Goal-Oriented Dialogue Systems, Machine Translation, Question Answering, Question Generation, Question-Generation, Short-Text Conversation, Text Summarization

We present bot{\#}1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI). The aim of the competition was to implement a bot capable of conversing with humans based on a given passage of text. To enable conversation, we implemented a set of skills for our bot, including chit-chat, topic detection, text summarization, question answering and question generation. The system has been trained in a supervised setting using a dialogue manager to select an appropriate skill for generating a response. The latter allows a developer to focus on the skill implementation rather than the finite state machine based dialog manager. The proposed system bot{\#}1337 won the competition with an average dialogue quality score of 2.78 out of 5 given by human evaluators. Source code and trained models for the bot{\#}1337 are available on GitHub.
AMR Beyond the Sentence: the Multi-sentence AMR corpus
Authors: Tim O{'}Gorman, Michael Regan, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Martha Palmer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Question Answering, Sentence

There are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference and similar phenomena (bridging and implicit roles) on top of gold Abstract Meaning Representations of sentence-level semantics. We present a new corpus of this annotation, with analysis of its quality, alongside a plausible baseline for comparison. It is hoped that this Multi-Sentence AMR corpus (MS-AMR) may become a feasible method for developing rich representations of document meaning, useful for tasks such as information extraction and question answering.
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
Authors: Lu Ji, Zhongyu Wei, Xiangkun Hu, Yang Liu, Qi Zhang, Xuanjing Huang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Persuasiveness

In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.
Structured Representation Learning for Online Debate Stance Prediction
Authors: Chang Li, Aldo Porco, Dan Goldwasser
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Prediction, Representation Learning

Online debates can help provide valuable information about various perspectives on a wide range of issues. However, understanding the stances expressed in these debates is a highly challenging task, which requires modeling both textual content and users{'} conversational interactions. Current approaches take a collective classification approach, which ignores the relationships between different debate topics. In this work, we suggest to view this task as a representation learning problem, and embed the text and authors jointly based on their interactions. We evaluate our model over the Internet Argumentation Corpus, and compare different approaches for structural information embedding. Experimental results show that our model can achieve significantly better results compared to previous competitive models.
Argumentation Synthesis following Rhetorical Strategies
Authors: Henning Wachsmuth, Manfred Stede, Roxanne El Baff, Khalid Al-Khatib, Maria Skeppstedt, Benno Stein
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Argument Mining, Ethics, Logical Reasoning

Persuasion is rarely achieved through a loose set of arguments alone. Rather, an effective delivery of arguments follows a rhetorical strategy, combining logical reasoning with appeals to ethics and emotion. We argue that such a strategy means to select, arrange, and phrase a set of argumentative discourse units. In this paper, we model rhetorical strategies for the computational synthesis of effective argumentation. In a study, we let 26 experts synthesize argumentative texts with different strategies for 10 topics. We find that the experts agree in the selection significantly more when following the same strategy. While the texts notably vary for different strategies, especially their arrangement remains stable. The results suggest that our model enables a strategical synthesis.
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering
Authors: Sayantan Mitra, Mohammed Hasanuzzaman, Sriparna Saha, Andy Way
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Clustering, Image Captioning, Multiobjective Optimization, MULTI-VIEW LEARNING, Sentiment Analysis

Current paper explores the use of multi-view learning for search result clustering. A web-snippet can be represented using multiple views. Apart from textual view cued by both the semantic and syntactic information, a complimentary view extracted from images contained in the web-snippets is also utilized in the current framework. A single consensus partitioning is finally obtained after consulting these two individual views by the deployment of a multiobjective based clustering technique. Several objective functions including the values of a cluster quality measure measuring the goodness of partitionings obtained using different views and an agreement-disagreement index, quantifying the amount of oneness among multiple views in generating partitionings are optimized simultaneously using AMOSA. In order to detect the number of clusters automatically, concepts of variable length solutions and a vast range of permutation operators are introduced in the clustering process. Finally, a set of alternative partitioning are obtained on the final Pareto front by the proposed multi-view based multiobjective technique. Experimental results by the proposed approach on several benchmark test datasets of SRC with respect to different performance metrics evidently establish the power of visual and text-based views in achieving better search result clustering.
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts
Authors: Yasuhide Miura, Ryuji Kano, Motoki Taniguchi, Tomoki Taniguchi, Shotaro Misawa, Tomoko Ohkuma
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Graph Learning, Structured Prediction

We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5{\%} in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.
AnlamVer: Semantic Model Evaluation Dataset for Turkish - Word Similarity and Relatedness
Authors: G{\"o}khan Ercan, Olcay Taner Y{\i}ld{\i}z
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Embeddings, Word Similarity

In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.
Arguments and Adjuncts in Universal Dependencies
Authors: Adam Przepi{\'o}rkowski, Agnieszka Patejuk
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

The aim of this paper is to argue for a coherent Universal Dependencies approach to the core vs. non-core distinction. We demonstrate inconsistencies in the current version 2 of UD in this respect {--} mostly resulting from the preservation of the argument{--}adjunct dichotomy despite the declared avoidance of this distinction {--} and propose a relatively conservative modification of UD that is free from these problems.
Authorless Topic Models: Biasing Models Away from Known Structure
Authors: Laure Thompson, David Mimno
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Document Classification, Topic Models, Word Embeddings

Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true. Some users want topic models that highlight differences between, for example, authors, but others seek more subtle connections across authors. We introduce three metrics for identifying topics that are highly correlated with metadata, and demonstrate that this problem affects between 30 and 50{\%} of the topics in models trained on two real-world collections, regardless of the size of the model. We find that we can predict which words cause this phenomenon and that by selectively subsampling these words we dramatically reduce topic-metadata correlation, improve topic stability, and maintain or even improve model quality.
Abbreviation Expander - a Web-based System for Easy Reading of Technical Documents
Authors: Manuel R. Ciosici, Ira Assent
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Abbreviations and acronyms are a part of textual communication in most domains. However, abbreviations are not necessarily defined in documents that employ them. Understanding all abbreviations used in a given document often requires extensive knowledge of the target domain and the ability to disambiguate based on context. This creates considerable entry barriers to newcomers and difficulties in automated document processing. Existing abbreviation expansion systems or tools require substantial technical knowledge for set up or make strong assumptions which limit their use in practice. Here, we present Abbreviation Expander, a system that builds on state of the art methods for identification of abbreviations, acronyms and their definitions and a novel disambiguator for abbreviation expansion in an easily accessible web-based solution.
The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation
Authors: Jan-Christoph Klie, Michael Bugert, Beto Boullosa, Richard Eckart de Castilho, Iryna Gurevych
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, Entity Linking, Knowledge Base Population, text annotation

We introduce INCEpTION, a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation). These tasks are very time consuming and demanding for annotators, especially when knowledge bases are used. We address these issues by developing an annotation platform that incorporates machine learning capabilities which actively assist and guide annotators. The platform is both generic and modular. It targets a range of research domains in need of semantic annotation, such as digital humanities, bioinformatics, or linguistics. INCEpTION is publicly available as open-source software.
JeSemE: Interleaving Semantics and Emotions in a Web Service for the Exploration of Language Change Phenomena
Authors: Johannes Hellrich, Sven Buechel, Udo Hahn
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis, Word Embeddings

We here introduce a substantially extended version of JeSemE, an interactive website for visually exploring computationally derived time-variant information on word meanings and lexical emotions assembled from five large diachronic text corpora. JeSemE is designed for scholars in the (digital) humanities as an alternative to consulting manually compiled, printed dictionaries for such information (if available at all). This tool uniquely combines state-of-the-art distributional semantics with a nuanced model of human emotions, two information streams we deem beneficial for a data-driven interpretation of texts in the humanities.