pwc-archive/papers-with-abstracts huggingface dataset:
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method
Authors: Song Jiang, Xiaotian Han
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Classification, Emotion Recognition, Ensemble Learning, regression

In this paper, we present a novel ensemble learning architecture for emotion intensity analysis, particularly a novel framework of ensemble method. The ensemble method has two stages and each stage includes several single machine learning models. In stage1, we employ both linear and nonlinear regression models to obtain a more diverse emotion intensity representation. In stage2, we use two regression models including linear regression and XGBoost. The result of stage1 serves as the input of stage2, so the two different type models (linear and non-linear) in stage2 can describe the input in two opposite aspects. We also added a method for analyzing and splitting multi-words hashtags and appending them to the emotion intensity corpus before feeding it to our model. Our model achieves 0.571 Pearson-measure for the average of four emotions.
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings
Authors: Vineet John, Olga Vechtomova
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Classification, Ensemble Learning, feature selection, regression, Word Embeddings

This paper describes the UWaterloo affect prediction system developed for EmoInt-2017. We delve into our feature selection approach for affect intensity, affect presence, sentiment intensity and sentiment presence lexica alongside pre-trained word embeddings, which are utilized to extract emotion intensity signals from tweets in an ensemble learning approach. The system employs emotion specific model training, and utilizes distinct models for each of the emotion corpora in isolation. Our system utilizes gradient boosted regression as the primary learning technique to predict the final emotion intensities.
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination
Authors: Davide Buscaldi, Belem Priego
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Recognition, Sentence, Sentiment Analysis, Word Embeddings

This paper presents the combined LIPN-UAM participation in the WASSA 2017 Shared Task on Emotion Intensity. In particular, the paper provides some highlights on the Tweetaneuse system that was presented to the shared task. We combined lexicon-based features with sentence-level vector representations to implement a random forest regressor.
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets
Authors: Vinayakumar R, Premjith B, Sachin Kumar S, Soman Kp, Poornach, Prabaharan ran
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Emotion Classification, Natural Language Inference, Question Answering

This working note presents the methodology used in deepCybErNet submission to the shared task on Emotion Intensities in Tweets (EmoInt) WASSA-2017. The goal of the task is to predict a real valued score in the range [0-1] for a particular tweet with an emotion type. To do this, we used Bag-of-Words and embedding based on recurrent network architecture. We have developed two systems and experiments are conducted on the Emotion Intensity shared Task 1 data base at WASSA-2017. A system which uses word embedding based on recurrent network architecture has achieved highest 5 fold cross-validation accuracy. This has used embedding with recurrent network to extract optimal features at tweet level and logistic regression for prediction. These methods are highly language independent and experimental results shows that the proposed methods are apt for predicting a real valued score in than range [0-1] for a given tweet with its emotion type.
Traversal-Free Word Vector Evaluation in Analogy Space
Authors: Xiaoyin Che, Nico Ring, Willi Raschkowski, Haojin Yang, Christoph Meinel
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

In this paper, we propose an alternative evaluating metric for word analogy questions (A to B is as C to D) in word vector evaluation. Different from the traditional method which predicts the fourth word by the given three, we measure the similarity directly on the {``}relations{''} of two pairs of given words, just as shifting the relation vectors into a new analogy space. Cosine and Euclidean distances are then calculated as measurements. Observation and experiments shows the proposed analogy space evaluation could offer a more comprehensive evaluating result on word vectors with word analogy questions. Meanwhile, computational complexity are remarkably reduced by avoiding traversing the vocabulary.
Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks
Authors: Jeremy Auguste, Arnaud Rey, Benoit Favre
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Word Embeddings, Word Similarity

This work presents a framework for word similarity evaluation grounded on cognitive sciences experimental data. Word pair similarities are compared to reaction times of subjects in large scale lexical decision and naming tasks under semantic priming. Results show that GloVe embeddings lead to significantly higher correlation with experimental measurements than other controlled and off-the-shelf embeddings, and that the choice of a training corpus is less important than that of the algorithm. Comparison of rankings with other datasets shows that the cognitive phenomenon covers more aspects than simply word relatedness or similarity.
Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games
Authors: Anmol Gulati, Kumar Krishna Agrawal
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Robot Navigation, Self-Driving Cars, text-based games, Translation, Word Embeddings

Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation.
Recognizing Textual Entailment in Twitter Using Word Embeddings
Authors: Octavia-Maria {\c{S}}ulea
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Information Retrieval, Machine Translation, Natural Language Inference, Question Answering, RTE, Text Classification, Word Embeddings

In this paper, we investigate the application of machine learning techniques and word embeddings to the task of Recognizing Textual Entailment (RTE) in Social Media. We look at a manually labeled dataset consisting of user generated short texts posted on Twitter (tweets) and related to four recent media events (the Charlie Hebdo shooting, the Ottawa shooting, the Sydney Siege, and the German Wings crash) and test to what extent neural techniques and embeddings are able to distinguish between tweets that entail or contradict each other or that claim unrelated things. We obtain comparable results to the state of the art in a train-test setting, but we show that, due to the noisy aspect of the data, results plummet in an evaluation strategy crafted to better simulate a real-life train-test scenario.
LCT-MALTA's Submission to RepEval 2017 Shared Task
Authors: Hoa Trong Vu, Thuong-Hai Pham, Xiaoyu Bai, Marc Tanti, Lonneke van der Plas, Albert Gatt
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Natural Language Inference, POS, Sentence Embeddings, Word Embeddings

System using BiLSTM and max pooling. Embedding is enhanced by POS, character and dependency info.
BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning
Authors: Yitong Li, Trevor Cohn, Timothy Baldwin
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Sentiment Analysis, Text Classification

This paper describes our submission to the sentiment analysis sub-task of {``}Build It, Break It: The Language Edition (BIBI){''}, on both the builder and breaker sides. As a builder, we use convolutional neural nets, trained on both phrase and sentence data. As a breaker, we use Q-learning to learn minimal change pairs, and apply a token substitution method automatically. We analyse the results to gauge the robustness of NLP systems.
An Adaptable Lexical Simplification Architecture for Major Ibero-Romance Languages
Authors: Daniel Ferr{\'e}s, Horacio Saggion, Xavier G{\'o}mez Guinovart
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Lexical Simplification, Text Simplification

Lexical Simplification is the task of reducing the lexical complexity of textual documents by replacing difficult words with easier to read (or understand) expressions while preserving the original meaning. The development of robust pipelined multilingual architectures able to adapt to new languages is of paramount importance in lexical simplification. This paper describes and evaluates a modular hybrid linguistic-statistical Lexical Simplifier that deals with the four major Ibero-Romance Languages: Spanish, Portuguese, Catalan, and Galician. The architecture of the system is the same for the four languages addressed, only the language resources used during simplification are language specific.
ACTSA: Annotated Corpus for Telugu Sentiment Analysis
Authors: S Mukku, eep Sricharan, Radhika Mamidi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentence, Sentiment Analysis

Sentiment analysis deals with the task of determining the polarity of a document or sentence and has received a lot of attention in recent years for the English language. With the rapid growth of social media these days, a lot of data is available in regional languages besides English. Telugu is one such regional language with abundant data available in social media, but it{'}s hard to find a labelled data of sentences for Telugu Sentiment Analysis. In this paper, we describe an effort to build a gold-standard annotated corpus of Telugu sentences to support Telugu Sentiment Analysis. The corpus, named ACTSA (Annotated Corpus for Telugu Sentiment Analysis) has a collection of Telugu sentences taken from different sources which were then pre-processed and manually annotated by native Telugu speakers using our annotation guidelines. In total, we have annotated 5457 sentences, which makes our corpus the largest resource currently available. The corpus and the annotation guidelines are made publicly available.
Breaking Sentiment Analysis of Movie Reviews
Authors: Ieva Stali{\=u}nait{\.e}, Ben Bonfil
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Sentiment Analysis, World Knowledge

The current paper covers several strategies we used to {`}break{'} predictions of sentiment analysis systems participating in the BLGNLP2017 workshop. Specifically, we identify difficulties of participating systems in understanding modals, subjective judgments, world-knowledge based references and certain differences in syntax and perspective.
Towards Full Text Shallow Discourse Relation Annotation: Experiments with Cross-Paragraph Implicit Relations in the PDTB
Authors: Rashmi Prasad, Katherine Forbes Riley, Alan Lee
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Discourse Parsing, Implicit Relations

Full text discourse parsing relies on texts comprehensively annotated with discourse relations. To this end, we address a significant gap in the inter-sentential discourse relations annotated in the Penn Discourse Treebank (PDTB), namely the class of cross-paragraph implicit relations, which account for 30{\%} of inter-sentential relations in the corpus. We present our annotation study to explore the incidence rate of adjacent vs. non-adjacent implicit relations in cross-paragraph contexts, and the relative degree of difficulty in annotating them. Our experiments show a high incidence of non-adjacent relations that are difficult to annotate reliably, suggesting the practicality of backing off from their annotation to reduce noise for corpus-based studies. Our resulting guidelines follow the PDTB adjacency constraint for implicits while employing an underspecified representation of non-adjacent implicits, and yield 62{\%} inter-annotator agreement on this task.
User-initiated Sub-dialogues in State-of-the-art Dialogue Systems
Authors: Staffan Larsson
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Dialogue Management, Spoken Dialogue Systems

We test state of the art dialogue systems for their behaviour in response to user-initiated sub-dialogues, i.e. interactions where a system question is responded to with a question or request from the user, who thus initiates a sub-dialogue. We look at sub-dialogues both within a single app (where the sub-dialogue concerns another topic in the original domain) and across apps (where the sub-dialogue concerns a different domain). The overall conclusion of the tests is that none of the systems can be said to deal appropriately with user-initiated sub-dialogues.
A Multimodal Dialogue System for Medical Decision Support inside Virtual Reality
Authors: Alex Prange, er, Margarita Chikobava, Peter Poller, Michael Barz, Daniel Sonntag
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We present a multimodal dialogue system that allows doctors to interact with a medical decision support system in virtual reality (VR). We integrate an interactive visualization of patient records and radiology image data, as well as therapy predictions. Therapy predictions are computed in real-time using a deep learning model.
Lexical Acquisition through Implicit Confirmations over Multiple Dialogues
Authors: Kohei Ono, Ryu Takeda, Eric Nichols, Mikio Nakano, Kazunori Komatani
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Chatbot, Task-Oriented Dialogue Systems

We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues. We develop an approach that makes implicit confirmation requests with an unknown term{'}s predicted category. Our approach does not degrade user experience with repetitive explicit confirmations, but the system has difficulty determining if information in the confirmation request can be correctly acquired. To overcome this challenge, we propose a method for determining whether or not the predicted category is correct, which is included in an implicit confirmation request. Our method exploits multiple user responses to implicit confirmation requests containing the same ontological category. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly predicted categories than when only single user responses were considered.
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders
Authors: Tsuneo Kato, Atsushi Nagai, Naoki Noda, Ryosuke Sumitomo, Jianming Wu, Seiichi Yamamoto
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Automatic Speech Recognition (ASR), Classification, General Classification, intent-classification, Intent Classification, Slot Filling, Speech Recognition, Word Embeddings

Recursive autoencoders (RAEs) for compositionality of a vector space model were applied to utterance intent classification of a smartphone-based Japanese-language spoken dialogue system. Though the RAEs express a nonlinear operation on the vectors of child nodes, the operation is considered to be different intrinsically depending on types of child nodes. To relax the difference, a data-driven untying of autoencoders (AEs) is proposed. The experimental result of the utterance intent classification showed an improved accuracy with the proposed method compared with the basic tied RAE and untied RAE based on a manual rule.
Automatic Measures to Characterise Verbal Alignment in Human-Agent Interaction
Authors: Guillaume Dubuisson Duplessis, Chlo{\'e} Clavel, L, Fr{\'e}d{\'e}ric ragin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

This work aims at characterising verbal alignment processes for improving virtual agent communicative capabilities. We propose computationally inexpensive measures of verbal alignment based on expression repetition in dyadic textual dialogues. Using these measures, we present a contrastive study between Human-Human and Human-Agent dialogues on a negotiation task. We exhibit quantitative differences in the strength and orientation of verbal alignment showing the ability of our approach to characterise important aspects of verbal alignment.
Demonstration of interactive teaching for end-to-end dialog control with hybrid code networks
Authors: Jason D. Williams, Lars Liden
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Dialog Learning, Entity Extraction using GAN, Intent Detection

This is a demonstration of interactive teaching for practical end-to-end dialog systems driven by a recurrent neural network. In this approach, a developer teaches the network by interacting with the system and providing on-the-spot corrections. Once a system is deployed, a developer can also correct mistakes in logged dialogs. This demonstration shows both of these teaching methods applied to dialog systems in three domains: pizza ordering, restaurant information, and weather forecasts.
Redundancy Localization for the Conversationalization of Unstructured Responses
Authors: Sebastian Krause, Mikhail Kozhevnikov, Eric Malmi, Daniele Pighin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Passage Retrieval, Question Answering, Retrieval

Conversational agents offer users a natural-language interface to accomplish tasks, entertain themselves, or access information. Informational dialogue is particularly challenging in that the agent has to hold a conversation on an open topic, and to achieve a reasonable coverage it generally needs to digest and present unstructured information from textual sources. Making responses based on such sources sound natural and fit appropriately into the conversation context is a topic of ongoing research, one of the key issues of which is preventing the agent{'}s responses from sounding repetitive. Targeting this issue, we propose a new task, known as redundancy localization, which aims to pinpoint semantic overlap between text passages. To help address it systematically, we formalize the task, prepare a public dataset with fine-grained redundancy labels, and propose a model utilizing a weak training signal defined over the results of a passage-retrieval system on web texts. The proposed model demonstrates superior performance compared to a state-of-the-art entailment model and yields encouraging results when applied to a real-world dialogue.
Attentive listening system with backchanneling, response generation and flexible turn-taking
Authors: Divesh Lala, Pierrick Milhorat, Koji Inoue, Masanari Ishida, Katsuya Takanashi, Tatsuya Kawahara
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Automatic Speech Recognition (ASR), Response Generation, Speech Recognition, Spoken Dialogue Systems

Attentive listening systems are designed to let people, especially senior people, keep talking to maintain communication ability and mental health. This paper addresses key components of an attentive listening system which encourages users to talk smoothly. First, we introduce continuous prediction of end-of-utterances and generation of backchannels, rather than generating backchannels after end-point detection of utterances. This improves subjective evaluations of backchannels. Second, we propose an effective statement response mechanism which detects focus words and responds in the form of a question or partial repeat. This can be applied to any statement. Moreover, a flexible turn-taking mechanism is designed which uses backchannels or fillers when the turn-switch is ambiguous. These techniques are integrated into a humanoid robot to conduct attentive listening. We test the feasibility of the system in a pilot experiment and show that it can produce coherent dialogues during conversation.
Natural Language Input for In-Car Spoken Dialog Systems: How Natural is Natural?
Authors: Patricia Braunger, Wolfgang Maier
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Recent spoken dialog systems are moving away from command and control towards a more intuitive and natural style of interaction. In order to choose an appropriate system design which allows the system to deal with naturally spoken user input, a definition of what exactly constitutes naturalness in user input is important. In this paper, we examine how different user groups naturally speak to an automotive spoken dialog system (SDS). We conduct a user study in which we collect freely spoken user utterances for a wide range of use cases in German. By means of a comparative study of the utterances from the study with interpersonal utterances, we provide criteria what constitutes naturalness in the user input of an state-of-the-art automotive SDS.
A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset
Authors: Shubham Agarwal, Marc Dymetman
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Data-to-Text Generation, Text Generation

We train a char2char model on the E2E NLG Challenge data, by exploiting {``}out-of-the-box{''} the recently released tfseq2seq framework, using some of the standard options offered by this tool. With minimal effort, and in particular without delexicalization, tokenization or lowercasing, the obtained raw predictions, according to a small scale human evaluation, are excellent on the linguistic side and quite reasonable on the adequacy side, the primary downside being the possible omissions of semantic material. However, in a significant number of cases (more than 70{\%}), a perfect solution can be found in the top-20 predictions, indicating promising directions for solving the remaining issues.
Interaction Quality Estimation Using Long Short-Term Memories
Authors: Niklas Rach, Wolfgang Minker, Stefan Ultes
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Spoken Dialogue Systems

For estimating the Interaction Quality (IQ) in Spoken Dialogue Systems (SDS), the dialogue history is of significant importance. Previous works included this information manually in the form of precomputed temporal features into the classification process. Here, we employ a deep learning architecture based on Long Short-Term Memories (LSTM) to extract this information automatically from the data, thus estimating IQ solely by using current exchange features. We show that it is thereby possible to achieve competitive results as in a scenario where manually optimized temporal features have been included.
DialPort, Gone Live: An Update After A Year of Development
Authors: Kyusong Lee, Tiancheng Zhao, Yulun Du, Edward Cai, Allen Lu, Eli Pincus, David Traum, Stefan Ultes, Lina M. Rojas-Barahona, Milica Gasic, Steve Young, Maxine Eskenazi
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Chatbot

DialPort collects user data for connected spoken dialog systems. At present six systems are linked to a central portal that directs the user to the applicable system and suggests systems that the user may be interested in. User data has started to flow into the system.