pwc-archive/papers-with-abstracts huggingface dataset:
MultiLing 2017 Overview
Authors: George Giannakopoulos, John Conroy, Jeff Kubina, Peter A. Rankel, Elena Lloret, Josef Steinberger, Marina Litvak, Benoit Favre
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Document Summarization

In this brief report we present an overview of the MultiLing 2017 effort and workshop, as implemented within EACL 2017. MultiLing is a community-driven initiative that pushes the state-of-the-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. This year the scope of the workshop was widened, bringing together researchers that work on summarization across sources, languages and genres. We summarize the main tasks planned and implemented this year, the contributions received, and we also provide insights on next steps.
Decoupling Encoder and Decoder Networks for Abstractive Document Summarization
Authors: Ying Xu, Jey Han Lau, Timothy Baldwin, Trevor Cohn
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Abstractive Text Summarization, Decoder, Document Summarization

Abstractive document summarization seeks to automatically generate a summary for a document, based on some abstract {``}understanding{''} of the original document. State-of-the-art techniques traditionally use attentive encoder{--}decoder architectures. However, due to the large number of parameters in these models, they require large training datasets and long training times. In this paper, we propose decoupling the encoder and decoder networks, and training them separately. We encode documents using an unsupervised document encoder, and then feed the document vector to a recurrent neural network decoder. With this decoupled architecture, we decrease the number of parameters in the decoder substantially, and shorten its training time. Experiments show that the decoupled model achieves comparable performance with state-of-the-art models for in-domain documents, but less well for out-of-domain documents.
Centroid-based Text Summarization through Compositionality of Word Embeddings
Authors: Gaetano Rossiello, Pierpaolo Basile, Giovanni Semeraro
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Document Summarization, Extractive Text Summarization, Multi-Document Summarization, Text Summarization, Word Embeddings

The textual similarity is a crucial aspect for many extractive text summarization methods. A bag-of-words representation does not allow to grasp the semantic relationships between concepts when comparing strongly related sentences with no words in common. To overcome this issue, in this paper we propose a centroid-based method for text summarization that exploits the compositional capabilities of word embeddings. The evaluations on multi-document and multilingual datasets prove the effectiveness of the continuous vector representation of words compared to the bag-of-words model. Despite its simplicity, our method achieves good performance even in comparison to more complex deep learning models. Our method is unsupervised and it can be adopted in other summarization tasks.
Query-based summarization using MDL principle
Authors: Marina Litvak, Natalia Vanetik
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Extractive Summarization, Language Modeling, Language Modelling, Query-Based Extractive Summarization, Text Summarization

Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results.
Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums
Authors: Lei Li, Liyuan Mao, Moye Chen
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Clustering, Sentence, Sentence Similarity, Sentiment Analysis, valid

Multiple grammatical and semantic features are adopted in content linking and argument/sentiment labeling for online forums in this paper. There are mainly two different methods for content linking. First, we utilize the deep feature obtained from Word Embedding Model in deep learning and compute sentence similarity. Second, we use multiple traditional features to locate candidate linking sentences, and then adopt a voting method to obtain the final result. LDA topic modeling is used to mine latent semantic feature and K-means clustering is implemented for argument labeling, while features from sentiment dictionaries and rule-based sentiment analysis are integrated for sentiment labeling. Experimental results have shown that our methods are valid.
Ultra-Concise Multi-genre Summarisation of Web2.0: towards Intelligent Content Generation
Authors: Elena Lloret, Ester Boldrini, Patricio Mart{\'\i}nez-Barco, Manuel Palomar
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Information Retrieval, Opinion Mining

The electronic Word of Mouth has become the most powerful communication channel thanks to the wide usage of the Social Media. Our research proposes an approach towards the production of automatic ultra-concise summaries from multiple Web 2.0 sources. We exploit user-generated content from reviews and microblogs in different domains, and compile and analyse four types of ultra-concise summaries: a)positive information, b) negative information; c) both or d) objective information. The appropriateness and usefulness of our model is demonstrated by its successful results and great potential in real-life applications, thus meaning a relevant advancement of the state-of-the-art approaches.
Machine Learning Approach to Evaluate MultiLingual Summaries
Authors: Samira Ellouze, Maher Jaoua, Lamia Hadrich belguith
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: BIG-bench Machine Learning, Ensemble Learning

The present paper introduces a new MultiLing text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and {``}ensemble learning{''} classifiers to build the best model. We have experimented our method in summary level evaluation where we evaluate each text summary separately. The correlation between built models and human score is better than the correlation between baselines and manual score.
Facebook sentiment: Reactions and Emojis
Authors: Ye Tian, Thiago Galery, Giulio Dulcinati, Emilia Molimpakis, Chao Sun
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

Emojis are used frequently in social media. A widely assumed view is that emojis express the emotional state of the user, which has led to research focusing on the expressiveness of emojis independent from the linguistic context. We argue that emojis and the linguistic texts can modify the meaning of each other. The overall communicated meaning is not a simple sum of the two channels. In order to study the meaning interplay, we need data indicating the overall sentiment of the entire message as well as the sentiment of the emojis stand-alone. We propose that Facebook Reactions are a good data source for such a purpose. FB reactions (e.g. {``}Love{''} and {``}Angry{''}) indicate the readers{'} overall sentiment, against which we can investigate the types of emojis used the comments under different reaction profiles. We present a data set of 21,000 FB posts (57 million reactions and 8 million comments) from public media pages across four countries.
Potential and Limitations of Cross-Domain Sentiment Classification
Authors: Jan Milan Deriu, Martin Weilenmann, Dirk Von Gruenigen, Mark Cieliebak
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

In this paper we investigate the cross-domain performance of a current state-of-the-art sentiment analysis systems. For this purpose we train a convolutional neural network (CNN) on data from different domains and evaluate its performance on other domains. Furthermore, we evaluate the usefulness of combining a large amount of different smaller annotated corpora to a large corpus. Our results show that more sophisticated approaches are required to train a system that works equally well on various domains.
Aligning Entity Names with Online Aliases on Twitter
Authors: Kevin McKelvey, Peter Goutzounis, Stephen da Cruz, Nathanael Chambers
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Coreference Resolution, Entity Linking, Sentiment Analysis

This paper presents new models that automatically align online aliases with their real entity names. Many research applications rely on identifying entity names in text, but people often refer to entities with unexpected nicknames and aliases. For example, The King and King James are aliases for Lebron James, a professional basketball player. Recent work on entity linking attempts to resolve mentions to knowledge base entries, like a wikipedia page, but linking is unfortunately limited to well-known entities with pre-built pages. This paper asks a more basic question: can aliases be aligned without background knowledge of the entity? Further, can the semantics surrounding alias mentions be used to inform alignments? We describe statistical models that make decisions based on the lexicographic properties of the aliases with their semantic context in a large corpus of tweets. We experiment on a database of Twitter users and their usernames, and present the first human evaluation for this task. Alignment accuracy approaches human performance at 81{\%}, and we show that while lexicographic features are most important, the semantic context of an alias further improves classification accuracy.
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis
Authors: Mark Cieliebak, Jan Milan Deriu, Dominic Egger, Fatih Uzdilli
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Named Entity Recognition (NER), Sentiment Analysis, Word Embeddings

In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.
Findings of the VarDial Evaluation Campaign 2017
Authors: Marcos Zampieri, Shervin Malmasi, Nikola Ljube{\v{s}}i{\'c}, Preslav Nakov, Ahmed Ali, J{\"o}rg Tiedemann, Yves Scherrer, No{\"e}mi Aepli
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dependency Parsing, Dialect Identification, Language Identification

We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EACL{'}2017. This year, we included four shared tasks: Discriminating between Similar Languages (DSL), Arabic Dialect Identification (ADI), German Dialect Identification (GDI), and Cross-lingual Dependency Parsing (CLP). A total of 19 teams submitted runs across the four tasks, and 15 of them wrote system description papers.
Computational analysis of Gondi dialects
Authors: Taraka Rama, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Pavel Sofroniev
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Word Alignment

This paper presents a computational analysis of Gondi dialects spoken in central India. We present a digitized data set of the dialect area, and analyze the data using different techniques from dialectometry, deep learning, and computational biology. We show that the methods largely agree with each other and with the earlier non-computational analyses of the language group.
Investigating Diatopic Variation in a Historical Corpus
Authors: Stefanie Dipper, S Waldenberger, ra
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

This paper investigates diatopic variation in a historical corpus of German. Based on equivalent word forms from different language areas, replacement rules and mappings are derived which describe the relations between these word forms. These rules and mappings are then interpreted as reflections of morphological, phonological or graphemic variation. Based on sample rules and mappings, we show that our approach can replicate results from historical linguistics. While previous studies were restricted to predefined word lists, or confined to single authors or texts, our approach uses a much wider range of data available in historical corpora.
Author Profiling at PAN: from Age and Gender Identification to Language Variety Identification (invited talk)
Authors: Paolo Rosso
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Author Profiling, Marketing, Sentiment Analysis

Author profiling is the study of how language is shared by people, a problem of growing importance in applications dealing with security, in order to understand who could be behind an anonymous threat message, and marketing, where companies may be interested in knowing the demographics of people that in online reviews liked or disliked their products. In this talk we will give an overview of the PAN shared tasks that since 2013 have been organised at CLEF and FIRE evaluation forums, mainly on age and gender identification in social media, although also personality recognition in Twitter as well as in code sources was also addressed. In 2017 the PAN author profiling shared task addresses jointly gender and language variety identification in Twitter where tweets have been annotated with authors{'} gender and their specific variation of their native language: English (Australia, Canada, Great Britain, Ireland, New Zealand, United States), Spanish (Argentina, Chile, Colombia, Mexico, Peru, Spain, Venezuela), Portuguese (Brazil, Portugal), and Arabic (Egypt, Gulf, Levantine, Maghrebi).
The similarity and Mutual Intelligibility between Amharic and Tigrigna Varieties
Authors: Tekabe Legesse Feleke
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4

The present study has examined the similarity and the mutual intelligibility between Amharic and Tigrigna using three tools namely Levenshtein distance, intelligibility test and questionnaires. The study has shown that both Tigrigna varieties have almost equal phonetic and lexical distances from Amharic. The study also indicated that Amharic speakers understand less than 50{\%} of the two varieties. Furthermore, the study showed that Amharic speakers are more positive about the Ethiopian Tigrigna variety than the Eritrean Variety. However, their attitude towards the two varieties does not have an impact on their intelligibility. The Amharic speakers{'} familiarity to the Tigrigna varieties is largely dependent on the genealogical relation between Amharic and the two Tigrigna varieties.
Why Catalan-Spanish Neural Machine Translation? Analysis, comparison and combination with standard Rule and Phrase-based technologies
Authors: Marta R. Costa-juss{\`a}
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Translation

Catalan and Spanish are two related languages given that both derive from Latin. They share similarities in several linguistic levels including morphology, syntax and semantics. This makes them particularly interesting for the MT task. Given the recent appearance and popularity of neural MT, this paper analyzes the performance of this new approach compared to the well-established rule-based and phrase-based MT systems. Experiments are reported on a large database of 180 million words. Results, in terms of standard automatic measures, show that neural MT clearly outperforms the rule-based and phrase-based MT system on in-domain test set, but it is worst in the out-of-domain test set. A naive system combination specially works for the latter. In-domain manual analysis shows that neural MT tends to improve both adequacy and fluency, for example, by being able to generate more natural translations instead of literal ones, choosing to the adequate target word when the source word has several translations and improving gender agreement. However, out-of-domain manual analysis shows how neural MT is more affected by unknown words or contexts.
Kurdish Interdialect Machine Translation
Authors: Hossein Hassani
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Machine Translation, Translation

This research suggests a method for machine translation among two Kurdish dialects. We chose the two widely spoken dialects, Kurmanji and Sorani, which are considered to be mutually unintelligible. Also, despite being spoken by about 30 million people in different countries, Kurdish is among less-resourced languages. The research used bi-dialectal dictionaries and showed that the lack of parallel corpora is not a major obstacle in machine translation between the two dialects. The experiments showed that the machine translated texts are comprehensible to those who do not speak the dialect. The research is the first attempt for inter-dialect machine translation in Kurdish and particularly could help in making online texts in one dialect comprehensible to those who only speak the target dialect. The results showed that the translated texts are in 71{\%} and 79{\%} cases rated as understandable for Kurmanji and Sorani respectively. They are rated as slightly-understandable in 29{\%} cases for Kurmanji and 21{\%} for Sorani.
Twitter Language Identification Of Similar Languages And Dialects Without Ground Truth
Authors: Jennifer Williams, Charlie Dagli
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: General Classification, Language Identification, Sentiment Analysis

We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geo-location, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA algorithm and langid.py. We show classifier performance on different versions of our dataset with high accuracy using only Twitter data, without ground truth, and very few training examples. We also show how Platt Scaling can be use to calibrate MIRA classifier output values into a probability distribution over candidate classes, making the output more intuitive. Our method allows for fine-grained distinctions between similar languages and dialects and allows us to rediscover the language composition of our Twitter dataset.
Multi-source morphosyntactic tagging for spoken Rusyn
Authors: Yves Scherrer, Achim Rabus
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Morphological Tagging, Part-Of-Speech Tagging

This paper deals with the development of morphosyntactic taggers for spoken varieties of the Slavic minority language Rusyn. As neither annotated corpora nor parallel corpora are electronically available for Rusyn, we propose to combine existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish and adapt them to Rusyn. Using MarMoT as tagging toolkit, we show that a tagger trained on a balanced set of the four source languages outperforms single language taggers by about 9{\%}, and that additional automatically induced morphosyntactic lexicons lead to further improvements. The best observed accuracies for Rusyn are 82.4{\%} for part-of-speech tagging and 75.5{\%} for full morphological tagging.
Identifying dialects with textual and acoustic cues
Authors: Abualsoud Hanani, Aziz Qaroush, Stephen Taylor
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Automatic Speech Recognition (ASR), Speech Recognition

We describe several systems for identifying short samples of Arabic or Swiss-German dialects, which were prepared for the shared task of the 2017 DSL Workshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The Swiss-German data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63{\%} on both the Swiss-German and Arabic dialects tasks.
Evaluating HeLI with Non-Linear Mappings
Authors: Tommi Jauhiainen, Krister Lind{\'e}n, Heidi Jauhiainen
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Identification, Position

In this paper we describe the non-linear mappings we used with the Helsinki language identification method, HeLI, in the 4th edition of the Discriminating between Similar Languages (DSL) shared task, which was organized as part of the VarDial 2017 workshop. Our SUKI team participated on the closed track together with 10 other teams. Our system reached the 7th position in the track. We describe the HeLI method and the non-linear mappings in mathematical notation. The HeLI method uses a probabilistic model with character n-grams and word-based backoff. We also describe our trials using the non-linear mappings instead of relative frequencies and we present statistics about the back-off function of the HeLI method.
A Perplexity-Based Method for Similar Languages Discrimination
Authors: Pablo Gamallo, Jose Ramom Pichel, I{\~n}aki Alegria
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Language Identification

This article describes the system submitted by the Citius{\_}Ixa{\_}Imaxin team to the VarDial 2017 (DSL and GDI tasks). The strategy underlying our system is based on a language distance computed by means of model perplexity. The best model configuration we have tested is a voting system making use of several $n$-grams models of both words and characters, even if word unigrams turned out to be a very competitive model with reasonable results in the tasks we have participated. An error analysis has been performed in which we identified many test examples with no linguistic evidences to distinguish among the variants.
Improving the Character Ngram Model for the DSL Task with BM25 Weighting and Less Frequently Used Feature Sets
Authors: Yves Bestgen
Date: 2017-04-01 00:00:00
Proceeding: WS 2017 4
Tasks: Dialect Identification, Language Identification

This paper describes the system developed by the Centre for English Corpus Linguistics (CECL) to discriminating similar languages, language varieties and dialects. Based on a SVM with character and POStag n-grams as features and the BM25 weighting scheme, it achieved 92.7{\%} accuracy in the Discriminating between Similar Languages (DSL) task, ranking first among eleven systems but with a lead over the next three teams of only 0.2{\%}. A simpler version of the system ranked second in the German Dialect Identification (GDI) task thanks to several ad hoc postprocessing steps. Complementary analyses carried out by a cross-validation procedure suggest that the BM25 weighting scheme could be competitive in this type of tasks, at least in comparison with the sublinear TF-IDF. POStag n-grams also improved the system performance.