pwc-archive/papers-with-abstracts huggingface dataset:
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles
Authors: Iacer Calixto, Daniel Stein, Evgeny Matusov, Sheila Castilho, Andy Way
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, NMT, Translation

In this paper, we study how humans perceive the use of images as an additional knowledge source to machine-translate user-generated product listings in an e-commerce company. We conduct a human evaluation where we assess how a multi-modal neural machine translation (NMT) model compares to two text-only approaches: a conventional state-of-the-art attention-based NMT and a phrase-based statistical machine translation (PBSMT) model. We evaluate translations obtained with different systems and also discuss the data set of user-generated product listings, which in our case comprises both product listings and associated images. We found that humans preferred translations obtained with a PBSMT system to both text-only and multi-modal NMT over 56{\%} of the time. Nonetheless, human evaluators ranked translations from a multi-modal NMT model as better than those of a text-only NMT over 88{\%} of the time, which suggests that images do help NMT in this use-case.
The BreakingNews Dataset
Authors: Arnau Ramisa, Fei Yan, Francesc Moreno-Noguer, Krystian Mikolajczyk
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Articles, Image Captioning, Sentiment Analysis

We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e.g. GPS coordinates and popularity metrics). The tenuous connection between the images and text in news data is appropriate to take work at the intersection of Computer Vision and Natural Language Processing to the next step, hence we hope this dataset will help spur progress in the field.
Automatic identification of head movements in video-recorded conversations: can words help?
Authors: Patrizia Paggio, Costanza Navarretta, Bart Jongejan
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Position, Temporal Sequences

We present an approach where an SVM classifier learns to classify head movements based on measurements of velocity, acceleration, and the third derivative of position with respect to time, jerk. Consequently, annotations of head movements are added to new video data. The results of the automatic annotation are evaluated against manual annotations in the same data and show an accuracy of 68{\%} with respect to these. The results also show that using jerk improves accuracy. We then conduct an investigation of the overlap between temporal sequences classified as either movement or non-movement and the speech stream of the person performing the gesture. The statistics derived from this analysis show that using word features may help increase the accuracy of the model.
Multi-Modal Fashion Product Retrieval
Authors: Antonio Rubio Romano, LongLong Yu, Edgar Simo-Serra, Francesc Moreno-Noguer
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Retrieval

Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a large-scale e-commerce dataset.
Metaphor Detection in a Poetry Corpus
Authors: Vaibhav Kesarwani, Diana Inkpen, Stan Szpakowicz, Chris Tanasescu
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Word Embeddings

Metaphor is indispensable in poetry. It showcases the poet{'}s creativity, and contributes to the overall emotional pertinence of the poem while honing its specific rhetorical impact. Previous work on metaphor detection relies on either rule-based or statistical models, none of them applied to poetry. Our method focuses on metaphor detection in a poetry corpus. It combines rule-based and statistical models (word embeddings) to develop a new classification system. Our system has achieved a precision of 0.759 and a recall of 0.804 in identifying one type of metaphor in poetry.
Machine Translation and Automated Analysis of the Sumerian Language
Authors: {\'E}milie Pag{\'e}-Perron, Maria Sukhareva, Ilya Khait, Christian Chiarcos
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Information Retrieval, Machine Translation, Retrieval, Translation

This paper presents a newly funded international project for machine translation and automated analysis of ancient cuneiform languages where NLP specialists and Assyriologists collaborate to create an information retrieval system for Sumerian. This research is conceived in response to the need to translate large numbers of administrative texts that are only available in transcription, in order to make them accessible to a wider audience. The methodology includes creation of a specialized NLP pipeline and also the use of linguistic linked open data to increase access to the results.
Investigating the Relationship between Literary Genres and Emotional Plot Development
Authors: Evgeny Kim, Sebastian Pad{\'o}, Roman Klinger
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: ARC, Genre classification

Literary genres are commonly viewed as being defined in terms of content and stylistic features. In this paper, we focus on one particular class of lexical features, namely emotion information, and investigate the hypothesis that emotion-related information correlates with particular genres. Using genre classification as a testbed, we compare a model that computes lexicon-based emotion scores globally for complete stories with a model that tracks emotion arcs through stories on a subset of Project Gutenberg with five genres. Our main findings are: (a), the global emotion model is competitive with a large-vocabulary bag-of-words genre classifier (80{\%}F1); (b), the emotion arc model shows a lower performance (59 {\%} F1) but shows complementary behavior to the global model, as indicated by a very good performance of an oracle model (94 {\%} F1) and an improved performance of an ensemble model (84 {\%} F1); (c), genres differ in the extent to which stories follow the same emotional arcs, with particularly uniform behavior for anger (mystery) and fear (adventures, romance, humor, science fiction).
Enjambment Detection in a Large Diachronic Corpus of Spanish Sonnets
Authors: Pablo Ruiz, Clara Mart{\'\i}nez Cant{\'o}n, Thierry Poibeau, Elena Gonz{\'a}lez-Blanco
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Enjambment takes place when a syntactic unit is broken up across two lines of poetry, giving rise to different stylistic effects. In Spanish literary studies, there are unclear points about the types of stylistic effects that can arise, and under which linguistic conditions. To systematically gather evidence about this, we developed a system to automatically identify enjambment (and its type) in Spanish. For evaluation, we manually annotated a reference corpus covering different periods. As a scholarly corpus to apply the tool, from public HTML sources we created a diachronic corpus covering four centuries of sonnets (3750 poems), and we analyzed the occurrence of enjambment across stanzaic boundaries in different periods. Besides, we found examples that highlight limitations in current definitions of enjambment.
Plotting Markson's ``Mistress''
Authors: Conor Kelleher, Mark Keane
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

The post-modern novel {``}Wittgenstein{'}s Mistress{''} by David Markson (1988) presents the reader with a very challenging non-linear narrative, that itself appears to one of the novel{'}s themes. We present a distant reading of this work designed to complement a close reading of it by David Foster Wallace (1990). Using a combination of text analysis, entity recognition and networks, we plot repetitive structures in the novel{'}s narrative relating them to its critical analysis.
Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German
Authors: Nina Seemann, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, Eyke H{\"u}llermeier
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA - a web-based annotation tool for historical and other non-standard language data - to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.
Phonological Soundscapes in Medieval Poetry
Authors: Christopher Hench
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

The oral component of medieval poetry was integral to its performance and reception. Yet many believe that the medieval voice has been forever lost, and any attempts at rediscovering it are doomed to failure due to scribal practices, manuscript mouvance, and linguistic normalization in editing practices. This paper offers a method to abstract from this noise and better understand relative differences in phonological soundscapes by considering syllable qualities. The presented syllabification method and soundscape analysis offer themselves as cross-disciplinary tools for low-resource languages. As a case study, we examine medieval German lyric and argue that the heavily debated lyrical {`}I{'} follows a unique trajectory through soundscapes, shedding light on the performance and practice of these poets.
An End-to-end Environment for Research Question-Driven Entity Extraction and Network Analysis
Authors: Andre Blessing, Nora Echelmeyer, Markus John, Nils Reiter
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Dependency Parsing, Entity Extraction using GAN

This paper presents an approach to extract co-occurrence networks from literary texts. It is a deliberate decision not to aim for a fully automatic pipeline, as the literary research questions need to guide both the definition of the nature of the things that co-occur as well as how to decide co-occurrence. We showcase the approach on a Middle High German romance, \textit{Parzival}. Manual inspection and discussion shows the huge impact various choices have.
Modeling intra-textual variation with entropy and surprisal: topical vs. stylistic patterns
Authors: Stefania Degaetano-Ortlieb, Elke Teich
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Articles

We present a data-driven approach to investigate intra-textual variation by combining entropy and surprisal. With this approach we detect linguistic variation based on phrasal lexico-grammatical patterns across sections of research articles. Entropy is used to detect patterns typical of specific sections. Surprisal is used to differentiate between more and less informationally-loaded patterns as well as type of information (topical vs. stylistic). While we here focus on research articles in biology/genetics, the methodology is especially interesting for digital humanities scholars, as it can be applied to any text type or domain and combined with additional variables (e.g. time, author or social group).
Finding a Character's Voice: Stylome Classification on Literary Characters
Authors: Liviu P. Dinu, Ana Sabina Uban
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Authorship Attribution, Classification, General Classification, Text Categorization

We investigate in this paper the problem of classifying the stylome of characters in a literary work. Previous research in the field of authorship attribution has shown that the writing style of an author can be characterized and distinguished from that of other authors automatically. In this paper we take a look at the less approached problem of how the styles of different characters can be distinguished, trying to verify if an author managed to create believable characters with individual styles. We present the results of some initial experiments developed on the novel {``}Liaisons Dangereuses{''}, showing that a simple bag of words model can be used to classify the characters.
An Ontology-Based Method for Extracting and Classifying Domain-Specific Compositional Nominal Compounds
Authors: Maria Pia di Buono
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Word Embeddings

In this paper, we present our preliminary study on an ontology-based method to extract and classify compositional nominal compounds in specific domains of knowledge. This method is based on the assumption that, applying a conceptual model to represent knowledge domain, it is possible to improve the extraction and classification of lexicon occurrences for that domain in a semi-automatic way. We explore the possibility of extracting and classifying a specific construction type (nominal compounds) spanning a specific domain (Cultural Heritage) and a specific language (Italian).
Speeding up corpus development for linguistic research: language documentation and acquisition in Romansh Tuatschin
Authors: G{\'e}raldine Walther, Beno{\^\i}t Sagot
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Language Acquisition, Spelling Correction

In this paper, we present ongoing work for developing language resources and basic NLP tools for an undocumented variety of Romansh, in the context of a language documentation and language acquisition project. Our tools are meant to improve the speed and reliability of corpus annotations for noisy data involving large amounts of code-switching, occurrences of child-speech and orthographic noise. Being able to increase the efficiency of language resource development for language documentation and acquisition research also constitutes a step towards solving the data sparsity issues with which researchers have been struggling.
Distantly Supervised POS Tagging of Low-Resource Languages under Extreme Data Sparsity: The Case of Hittite
Authors: Maria Sukhareva, Francesco Fuscagni, Johannes Daxenberger, Susanne G{\"o}rke, Doris Prechel, Iryna Gurevych
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Information Retrieval, Machine Translation, POS, POS Tagging

This paper presents a statistical approach to automatic morphosyntactic annotation of Hittite transcripts. Hittite is an extinct Indo-European language using the cuneiform script. There are currently no morphosyntactic annotations available for Hittite, so we explored methods of distant supervision. The annotations were projected from parallel German translations of the Hittite texts. In order to reduce data sparsity, we applied stemming of German and Hittite texts. As there is no off-the-shelf Hittite stemmer, a stemmer for Hittite was developed for this purpose. The resulting annotation projections were used to train a POS tagger, achieving an accuracy of 69{\%} on a test sample. To our knowledge, this is the first attempt of statistical POS tagging of a cuneiform language.
A Dataset for Sanskrit Word Segmentation
Authors: Amrith Krishna, Pavan Kumar Satuluri, Pawan Goyal
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Segmentation, Sentence, Transfer Learning

The last decade saw a surge in digitisation efforts for ancient manuscripts in Sanskrit. Due to various linguistic peculiarities inherent to the language, even the preliminary tasks such as word segmentation are non-trivial in Sanskrit. Elegant models for Word Segmentation in Sanskrit are indispensable for further syntactic and semantic processing of the manuscripts. Current works in word segmentation for Sanskrit, though commendable in their novelty, often have variations in their objective and evaluation criteria. In this work, we set the record straight. We formally define the objectives and the requirements for the word segmentation task. In order to encourage research in the field and to alleviate the time and effort required in pre-processing, we release a dataset of 115,000 sentences for word segmentation. For each sentence in the dataset we include the input character sequence, ground truth segmentation, and additionally lexical and morphological information about all the phonetically possible segments for the given sentence. In this work, we also discuss the linguistic considerations made while generating the candidate space of the possible segments.
Lexical Correction of Polish Twitter Political Data
Authors: Maciej Ogrodniczuk, Mateusz Kope{\'c}
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Entity Extraction using GAN, Lemmatization, Spelling Correction

Language processing architectures are often evaluated in near-to-perfect conditions with respect to processed content. The tools which perform sufficiently well on electronic press, books and other type of non-interactive content may poorly handle littered, colloquial and multilingual textual data which make the majority of communication today. This paper aims at investigating how Polish Twitter data (in a slightly controlled {`}political{'} flavour) differs from expectation of linguistic tools and how they could be corrected to be ready for processing by standard language processing chains available for Polish. The setting includes specialised components for spelling correction of tweets as well as hashtag and username decoding.
Target word prediction and paraphasia classification in spoken discourse
Authors: Joel Adams, Steven Bedrick, Gerasimos Fergadiotis, Kyle Gorman, Jan van Santen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Classification, General Classification, Prediction, Semantic Textual Similarity, Spoken Language Understanding

We present a system for automatically detecting and classifying phonologically anomalous productions in the speech of individuals with aphasia. Working from transcribed discourse samples, our system identifies neologisms, and uses a combination of string alignment and language models to produce a lattice of plausible words that the speaker may have intended to produce. We then score this lattice according to various features, and attempt to determine whether the anomalous production represented a phonemic error or a genuine neologism. This approach has the potential to be expanded to consider other types of paraphasic errors, and could be applied to a wide variety of screening and therapeutic applications.
Extracting Drug-Drug Interactions with Attention CNNs
Authors: Masaki Asada, Makoto Miwa, Yutaka Sasaki
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Feature Engineering, General Classification, Relation Classification, Relation Extraction, Sentence

We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12{\%}, which is competitive with the state-of-the-art models.
Stacking With Auxiliary Features for Entity Linking in the Medical Domain
Authors: Nazneen Fatema Rajani, Mihaela Bornea, Ken Barker
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Entity Linking, Hallucination

Linking spans of natural language text to concepts in a structured source is an important task for many problems. It allows intelligent systems to leverage rich knowledge available in those sources (such as concept properties and relations) to enhance the semantics of the mentions of these concepts in text. In the medical domain, it is common to link text spans to medical concepts in large, curated knowledge repositories such as the Unified Medical Language System. Different approaches have different strengths: some are precision-oriented, some recall-oriented; some better at considering context but more prone to hallucination. The variety of techniques suggests that ensembling could outperform component technologies at this task. In this paper, we describe our process for building a Stacking ensemble using additional, auxiliary features for Entity Linking in the medical domain. We report experiments that show that naive ensembling does not always outperform component Entity Linking systems, that stacking usually outperforms naive ensembling, and that auxiliary features added to the stacker further improve its performance on three distinct datasets. Our best model produces state-of-the-art results on several medical datasets.
Results of the fifth edition of the BioASQ Challenge
Authors: Anastasios Nentidis, Konstantinos Bougiatiotis, Anastasia Krithara, Georgios Paliouras, Ioannis Kakadiaris
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Information Retrieval, Question Answering

The goal of the BioASQ challenge is to engage researchers into creating cuttingedge biomedical information systems. Specifically, it aims at the promotion of systems and methodologies that are able to deal with a plethora of different tasks in the biomedical domain. This is achieved through the organization of challenges. The fifth challenge consisted of three tasks: semantic indexing, question answering and a new task on information extraction. In total, 29 teams with more than 95 systems participated in the challenge. Overall, as in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the art systems are continuously improving, pushing the frontier of research.
Tackling Biomedical Text Summarization: OAQA at BioASQ 5B
Authors: Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Answer Generation, Clustering, Extractive Summarization, Question Answering, Sentence, Sentence Compression, Text Summarization

In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data. We describe our techniques with an emphasis on ideal answer generation, where the goal is to produce a relevant, precise, non-redundant, query-oriented summary from multiple relevant documents. We make use of extractive summarization techniques to address this task and experiment with different biomedical ontologies and various algorithms including agglomerative clustering, Maximum Marginal Relevance (MMR) and sentence compression. We propose a novel word embedding based tf-idf similarity metric and a soft positional constraint which improve our system performance. We evaluate our techniques on test batch 4 from the fourth edition of the challenge. Our best system achieves a ROUGE-2 score of 0.6534 and ROUGE-SU4 score of 0.6536.