pwc-archive/papers-with-abstracts huggingface dataset:
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper
Authors: Nut Limsopatham, Oleg Rokhlenko, David Carmel
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Chatbot, Information Retrieval, Machine Translation, Position, Retrieval, speech-recognition, Speech Recognition

Recent advances in automatic speech recognition lead toward enabling a voice conversation between a human user and an intelligent virtual assistant. This provides a potential foundation for developing artificial personal shoppers for e-commerce websites, such as Alibaba, Amazon, and eBay. Personal shoppers are valuable to the on-line shops as they enhance user engagement and trust by promptly dealing with customers{'} questions and concerns. Developing an artificial personal shopper requires the agent to leverage knowledge about the customer and products, while interacting with the customer in a human-like conversation. In this position paper, we motivate and describe \textit{the artificial personal shopper task}, and then address a research agenda for this task by adapting and advancing existing information retrieval and natural language processing technologies.
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management
Authors: Atsushi Saito
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Deep Reinforcement Learning, Dialogue Management, Information Retrieval, Management, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

Learning from sparse and delayed reward is a central issue in reinforcement learning. In this paper, to tackle reward sparseness problem of task oriented dialogue management, we propose a curriculum based approach on the number of slots of user goals. This curriculum makes it possible to learn dialogue management for sets of user goals with large number of slots. We also propose a dialogue policy based on progressive neural networks whose modules with parameters are appended with previous parameters fixed as the curriculum proceeds, and this policy improves performances over the one with single set of parameters.
Data Augmentation for Neural Online Chats Response Selection
Authors: Wenchao Du, Alan Black
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Data Augmentation

Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models. We investigate two data augmentation proxies, permutation and flipping, for neural dialog response selection task on various models over multiple datasets, including both Chinese and English languages. Different from standard data augmentation techniques, our method combines the original and synthesized data for prediction. Empirical results show that our approach can gain 1 to 3 recall-at-1 points over baseline models in both full-scale and small-scale settings.
Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
Authors: Samuel Louvan, Bernardo Magnini
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Domain Adaptation, Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Understanding, NER, slot-filling, Slot Filling, Transfer Learning

Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art. In particular, NER is effective when supervised at the lower layer of the model. For low-resource scenarios, we found that MTL is effective for one dataset.
Efficient Computation of Implicational Universals in Constraint-Based Phonology Through the Hyperplane Separation Theorem
Authors: Giorgio Magri
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

This paper focuses on the most basic \textit{implicational universals} in phonological theory, called \textit{T-orders} after Anttila and Andrus (2006). It develops necessary and sufficient constraint characterizations of T-orders within \textit{Harmonic Grammar} and \textit{Optimality Theory}. These conditions rest on the rich convex geometry underlying these frameworks. They are phonologically intuitive and have significant algorithmic implications.
Lexical Networks in !Xung
Authors: Syed-Amad Hussain, Micha Elsner, Am Miller, a
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Retrieval

We investigate the lexical network properties of the large phoneme inventory Southern African language Mangetti Dune !Xung as it compares to English and other commonly-studied languages. Lexical networks are graphs in which nodes (words) are linked to their minimal pairs; global properties of these networks are believed to mediate lexical access in the minds of speakers. We show that the network properties of !Xung are within the range found in previously-studied languages. By simulating data ({''}pseudolexicons{''}) with varying levels of phonotactic structure, we find that the lexical network properties of !Xung diverge from previously-studied languages when fewer phonotactic constraints are retained. We conclude that lexical network properties are representative of an underlying cognitive structure which is necessary for efficient word retrieval and that the phonotactics of !Xung may be shaped by a selective pressure which preserves network properties within this cognitively useful range.
Acoustic Word Disambiguation with Phonogical Features in Danish ASR
Authors: Andreas S{\o}eborg Kirkedal
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Phonological features can indicate word class and we can use word class information to disambiguate both homophones and homographs in automatic speech recognition (ASR). We show Danish st{\o}d can be predicted from speech and used to improve ASR. We discover which acoustic features contain the signal of st{\o}d, how to use these features to predict st{\o}d and how we can make use of st{\o}d and st{\o}dpredictive acoustic features to improve overall ASR accuracy and decoding speed. In the process, we discover acoustic features that are novel to the phonetic characterisation of st{\o}d.
Complementary Strategies for Low Resourced Morphological Modeling
Authors: Alex Erdmann, er, Nizar Habash
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Morphological Analysis, Word Embeddings

Morphologically rich languages are challenging for natural language processing tasks due to data sparsity. This can be addressed either by introducing out-of-context morphological knowledge, or by developing machine learning architectures that specifically target data sparsity and/or morphological information. We find these approaches to complement each other in a morphological paradigm modeling task in Modern Standard Arabic, which, in addition to being morphologically complex, features ubiquitous ambiguity, exacerbating sparsity with noise. Given a small number of out-of-context rules describing closed class morphology, we combine them with word embeddings leveraging subword strings and noise reduction techniques. The combination outperforms both approaches individually by about 20{\%} absolute. While morphological resources already exist for Modern Standard Arabic, our results inform how comparable resources might be constructed for non-standard dialects or any morphologically rich, low resourced language, given scarcity of time and funding.
Modeling Reduplication with 2-way Finite-State Transducers
Authors: Hossep Dolatian, Jeffrey Heinz
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

This article describes a novel approach to the computational modeling of reduplication. Reduplication is a well-studied linguistic phenomenon. However, it is often treated as a stumbling block within finite-state treatments of morphology. Most finite-state implementations of computational morphology cannot adequately capture the productivity of unbounded copying in reduplication, nor can they adequately capture bounded copying. We show that an understudied type of finite-state machines, two-way finite-state transducers (2-way FSTs), captures virtually all reduplicative processes, including total reduplication. 2-way FSTs can model reduplicative typology in a way which is convenient, easy to design and debug in practice, and linguistically-motivated. By virtue of being finite-state, 2-way FSTs are likewise incorporable into existing finite-state systems and programs. A small but representative typology of reduplicative processes is described in this article, alongside their corresponding 2-way FST models.
Automatically Tailoring Unsupervised Morphological Segmentation to the Language
Authors: Esk, Ramy er, Owen Rambow, Smar Muresan, a
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Segmentation, Speech Recognition

Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies. Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages. Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration. However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages. We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages.
A Comparison of Entity Matching Methods between English and Japanese Katakana
Authors: Michiharu Yamashita, Hideki Awashima, Hidekazu Oiwa
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Transliteration

Japanese Katakana is one component of the Japanese writing system and is used to express English terms, loanwords, and onomatopoeia in Japanese characters based on the phonemes. The main purpose of this research is to find the best entity matching methods between English and Katakana. We built two research questions to clarify which types of entity matching systems works better than others. The first question is what transliteration should be used for conversion. We need to transliterate English or Katakana terms into the same form in order to compute the string similarity. We consider five conversions that transliterate English to Katakana directly, Katakana to English directly, English to Katakana via phoneme, Katakana to English via phoneme, and both English and Katakana to phoneme. The second question is what should be used for the similarity measure at entity matching. To investigate the problem, we choose six methods, which are Overlap Coefficient, Cosine, Jaccard, Jaro-Winkler, Levenshtein, and the similarity of the phoneme probability predicted by RNN. Our results show that 1) matching using phonemes and conversion of Katakana to English works better than other methods, and 2) the similarity of phonemes outperforms other methods while other similarity score is changed depending on data and models.
Seq2Seq Models with Dropout can Learn Generalizable Reduplication
Authors: Br Prickett, on, Aaron Traylor, Joe Pater
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Natural language reduplication can pose a challenge to neural models of language, and has been argued to require variables (Marcus et al., 1999). Sequence-to-sequence neural networks have been shown to perform well at a number of other morphological tasks (Cotterell et al., 2016), and produce results that highly correlate with human behavior (Kirov, 2017; Kirov {\&} Cotterell, 2018) but do not include any explicit variables in their architecture. We find that they can learn a reduplicative pattern that generalizes to novel segments if they are trained with dropout (Srivastava et al., 2014). We argue that this matches the scope of generalization observed in human reduplication.
Phonetic Vector Representations for Sound Sequence Alignment
Authors: Pavel Sofroniev, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

This study explores a number of data-driven vector representations of the IPA-encoded sound segments for the purpose of sound sequence alignment. We test the alternative representations based on the alignment accuracy in the context of computational historical linguistics. We show that the data-driven methods consistently do better than linguistically-motivated articulatory-acoustic features. The similarity scores obtained using the data-driven representations in a monolingual context, however, performs worse than the state-of-the-art distance (or similarity) scoring methods proposed in earlier studies of computational historical linguistics. We also show that adapting representations to the task at hand improves the results, yielding alignment accuracy comparable to the state of the art methods.
Sounds Wilde. Phonetically Extended Embeddings for Author-Stylized Poetry Generation
Authors: Aleksey Tikhonov, Ivan Yamshchikov
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Attribute, Language Modeling, Language Modelling, Text Generation, Word Embeddings

This paper addresses author-stylized text generation. Using a version of a language model with extended phonetic and semantic embeddings for poetry generation we show that phonetics has comparable contribution to the overall model performance as the information on the target author. Phonetic information is shown to be important for English and Russian language. Humans tend to attribute machine generated texts to the target author.
On Hapax Legomena and Morphological Productivity
Authors: Janet Pierrehumbert, Ramon Granell
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Quantifying and predicting morphological productivity is a long-standing challenge in corpus linguistics and psycholinguistics. The same challenge reappears in natural language processing in the context of handling words that were not seen in the training set (out-of-vocabulary, or OOV, words). Prior research showed that a good indicator of the productivity of a morpheme is the number of words involving it that occur exactly once (the \textit{hapax legomena}). A technical connection was adduced between this result and Good-Turing smoothing, which assigns probability mass to unseen events on the basis of the simplifying assumption that word frequencies are stationary. In a large-scale study of 133 affixes in Wikipedia, we develop evidence that success in fact depends on tapping the frequency range in which the assumptions of Good-Turing are violated.
A Morphological Analyzer for Shipibo-Konibo
Authors: Ronald Cardenas, Daniel Zeman
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Lemmatization, Machine Translation, Speech Recognition

We present a fairly complete morphological analyzer for Shipibo-Konibo, a low-resourced native language spoken in the Amazonian region of Peru. We resort to the robustness of finite-state systems in order to model the complex morphosyntax of the language. Evaluation over raw corpora shows promising coverage of grammatical phenomena, limited only by the scarce lexicon. We make this tool freely available so as to aid the production of annotated corpora and impulse further research in native languages of Peru.
An Arabic Morphological Analyzer and Generator with Copious Features
Authors: Dima Taji, Salam Khalifa, Ossama Obeid, Fadhl Eryani, Nizar Habash
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

We introduce CALIMA-Star, a very rich Arabic morphological analyzer and generator that provides functional and form-based morphological features as well as built-in tokenization, phonological representation, lexical rationality and much more. This tool includes a fast engine that can be easily integrated into other systems, as well as an easy-to-use API and a web interface. CALIMA-Star also supports morphological reinflection. We evaluate CALIMA-Star against four commonly used analyzers for Arabic in terms of speed and morphological content.
Sanskrit n-Retroflexion is Input-Output Tier-Based Strictly Local
Authors: Thomas Graf, Connor Mayer
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Sanskrit /n/-retroflexion is one of the most complex segmental processes in phonology. While it is still star-free, it does not fit in any of the subregular classes that are commonly entertained in the literature. We show that when construed as a phonotactic dependency, the process fits into a class we call \textit{input-output tier-based strictly local} (IO-TSL), a natural extension of the familiar class TSL. IO-TSL increases the power of TSL{'}s tier projection function by making it an input-output strictly local transduction. Assuming that /n/-retroflexion represents the upper bound on the complexity of segmental phonology, this shows that all of segmental phonology can be captured by combining the intuitive notion of tiers with the independently motivated machinery of strictly local mappings.
Phonological Features for Morphological Inflection
Authors: Adam Wiemerslage, Miikka Silfverberg, Mans Hulden
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Morphological Analysis, Morphological Inflection, Transfer Learning

Modeling morphological inflection is an important task in Natural Language Processing. In contrast to earlier work that has largely used orthographic representations, we experiment with this task in a phonetic character space, representing inputs as either IPA segments or bundles of phonological distinctive features. We show that both of these inputs, somewhat counterintuitively, achieve similar accuracies on morphological inflection, slightly lower than orthographic models. We conclude that providing detailed phonological representations is largely redundant when compared to IPA segments, and that articulatory distinctions relevant for word inflection are already latently present in the distributional properties of many graphemic writing systems.
Extracting Morphophonology from Small Corpora
Authors: Marina Ermolaeva
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Probabilistic approaches have proven themselves well in learning phonological structure. In contrast, theoretical linguistics usually works with deterministic generalizations. The goal of this paper is to explore possible interactions between information-theoretic methods and deterministic linguistic knowledge and to examine some ways in which both can be used in tandem to extract phonological and morphophonological patterns from a small annotated dataset. Local and nonlocal processes in Mishar Tatar (Turkic/Kipchak) are examined as a case study.
Football and Beer - a Social Media Analysis on Twitter in Context of the FIFA Football World Cup 2018
Authors: Rol Roller, Philippe Thomas, Sven Schmeier
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

In many societies alcohol is a legal and common recreational substance and socially accepted. Alcohol consumption often comes along with social events as it helps people to increase their sociability and to overcome their inhibitions. On the other hand we know that increased alcohol consumption can lead to serious health issues, such as cancer, cardiovascular diseases and diseases of the digestive system, to mention a few. This work examines alcohol consumption during the FIFA Football World Cup 2018, particularly the usage of alcohol related information on Twitter. For this we analyse the tweeting behaviour and show that the tournament strongly increases the interest in beer. Furthermore we show that countries who had to leave the tournament at early stage might have done something good to their fans as the interest in beer decreased again.
Stance-Taking in Topics Extracted from Vaccine-Related Tweets and Discussion Forum Posts
Authors: Maria Skeppstedt, Manfred Stede, Andreas Kerren
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

The occurrence of stance-taking towards vaccination was measured in documents extracted by topic modelling from two different corpora, one discussion forum corpus and one tweet corpus. For some of the topics extracted, their most closely associated documents contained a proportion of vaccine stance-taking texts that exceeded the corpus average by a large margin. These extracted document sets would, therefore, form a useful resource in a process for computer-assisted analysis of argumentation on the subject of vaccination.
Identifying Depression on Reddit: The Effect of Training Data
Authors: Inna Pirina, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification

This paper presents a set of classification experiments for identifying depression in posts gathered from social media platforms. In addition to the data gathered previously by other researchers, we collect additional data from the social media platform Reddit. Our experiments show promising results for identifying depression from social media texts. More importantly, however, we show that the choice of corpora is crucial in identifying depression and can lead to misleading conclusions in case of poor choice of data.
Overview of the Third Social Media Mining for Health (SMM4H) Shared Tasks at EMNLP 2018
Authors: Davy Weissenbacher, Abeed Sarker, Michael J. Paul, Gonzalez-Hern, Graciela ez
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Classification, General Classification, Task 2, Text Classification

The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets. The third execution of the SMM4H shared tasks, co-hosted with EMNLP-2018, comprised of four subtasks. These subtasks involve annotated user posts from Twitter (tweets) and focus on the (i) automatic classification of tweets mentioning a drug name, (ii) automatic classification of tweets containing reports of first-person medication intake, (iii) automatic classification of tweets presenting self-reports of adverse drug reaction (ADR) detection, and (iv) automatic classification of vaccine behavior mentions in tweets. A total of 14 teams participated and 78 system runs were submitted (23 for task 1, 20 for task 2, 18 for task 3, 17 for task 4).
Changes in Psycholinguistic Attributes of Social Media Users Before, During, and After Self-Reported Influenza Symptoms
Authors: Lucie Flekova, Vasileios Lampos, Ingemar Cox
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Descriptive

Previous research has linked psychological and social variables to physical health. At the same time, psychological and social variables have been successfully predicted from the language used by individuals in social media. In this paper, we conduct an initial exploratory study linking these two areas. Using the social media platform of Twitter, we identify users self-reporting symptoms that are descriptive of influenza-like illness (ILI). We analyze the tweets of those users in the periods before, during, and after the reported symptoms, exploring emotional, cognitive, and structural components of language. We observe a post-ILI increase in social activity and cognitive processes, possibly supporting previous offline findings linking more active social activities and stronger cognitive coping skills to a better immune status.