pwc-archive/papers-with-abstracts huggingface dataset:
A Corpus of Corporate Annual and Social Responsibility Reports: 280 Million Tokens of Balanced Organizational Writing
Authors: Sebastian G. M. H{\"a}ndschke, Sven Buechel, Jan Goldenstein, Philipp Poschmann, Tinghui Duan, Peter Walgenbach, Udo Hahn
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Management

We introduce JOCo, a novel text corpus for NLP analytics in the field of economics, business and management. This corpus is composed of corporate annual and social responsibility reports of the top 30 US, UK and German companies in the major (DJIA, FTSE 100, DAX), middle-sized (S{\&}P 500, FTSE 250, MDAX) and technology (NASDAQ, FTSE AIM 100, TECDAX) stock indices, respectively. Altogether, this adds up to 5,000 reports from 270 companies headquartered in three of the world{'}s most important economies. The corpus spans a time frame from 2000 up to 2015 and contains, in total, 282M tokens. We also feature JOCo in a small-scale experiment to demonstrate its potential for NLP-fueled studies in economics, business and management research.
Word Embeddings-Based Uncertainty Detection in Financial Disclosures
Authors: Christoph Kilian Theil, Sanja {\v{S}}tajner, Heiner Stuckenschmidt
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Sentence Classification, Word Embeddings

In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures. Leveraging general-domain and domain-specific word embedding models, we automatically expand an existing dictionary of uncertainty triggers. We furthermore examine how an expert filtering affects the quality of such an expansion. We show that the dictionary expansions significantly improve regressions on stock return volatility. Lastly, we prove that the expansions significantly boost the automatic detection of uncertain sentences.
A Simple End-to-End Question Answering Model for Product Information
Authors: Tuan Lai, Trung Bui, Sheng Li, Nedim Lipka
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Answer Selection, Question Answering

When evaluating a potential product purchase, customers may have many questions in mind. They want to get adequate information to determine whether the product of interest is worth their money. In this paper we present a simple deep learning model for answering questions regarding product facts and specifications. Given a question and a product specification, the model outputs a score indicating their relevance. To train and evaluate our proposed model, we collected a dataset of 7,119 questions that are related to 153 different products. Experimental results demonstrate that {--}despite its simplicity{--} the performance of our model is shown to be comparable to a more complex state-of-the-art baseline.
Sentence Classification for Investment Rules Detection
Authors: Youness Mansar, Sira Ferradans
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Classification, General Classification, Sentence, Sentence Classification

In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production.
Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain
Authors: Tobias Daudert, Paul Buitelaar, Sapna Negi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

With the rising popularity of social media in the society and in research, analysing texts short in length, such as microblogs, becomes an increasingly important task. As a medium of communication, microblogs carry peoples sentiments and express them to the public. Given that sentiments are driven by multiple factors including the news media, the question arises if the sentiment expressed in news and the news article themselves can be leveraged to detect and classify sentiment in microblogs. Prior research has highlighted the impact of sentiments and opinions on the market dynamics, making the financial domain a prime case study for this approach. Therefore, this paper describes ongoing research dealing with the exploitation of news contained sentiment to improve microblog sentiment classification in a financial context.
Implicit and Explicit Aspect Extraction in Financial Microblogs
Authors: Thomas Gaillat, Bernardo Stearns, Gopal Sridhar, Ross McDermott, Manel Zarrouk, Brian Davis
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, General Classification, Sentiment Analysis

This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsupervised methods to assign predefined categories at message level. Results on 7 aspect classes show 0.71 accuracy, while the 32 class classification gives 0.82 accuracy for messages containing explicit aspects and 0.35 for implicit aspects.
Unsupervised Word Influencer Networks from News Streams
Authors: Ananth Balashankar, Sun Chakraborty, an, Lakshminarayanan Subramanian
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Relationship Extraction (Distant Supervised), Stock Price Prediction

In this paper, we propose a new unsupervised learning framework to use news events for predicting trends in stock prices. We present Word Influencer Networks (WIN), a graph framework to extract longitudinal temporal relationships between any pair of informative words from news streams. Using the temporal occurrence of words, WIN measures how the appearance of one word in a news stream influences the emergence of another set of words in the future. The latent word-word influencer relationships in WIN are the building blocks for causal reasoning and predictive modeling. We demonstrate the efficacy of WIN by using it for unsupervised extraction of latent features for stock price prediction and obtain 2 orders lower prediction error compared to a similar causal graph based method. WIN discovered influencer links from seemingly unrelated words from topics like politics to finance. WIN also validated 67{\%} of the causal evidence found manually in the text through a direct edge and the rest 33{\%} through a path of length 2.
Joint Part-of-Speech and Language ID Tagging for Code-Switched Data
Authors: Victor Soto, Julia Hirschberg
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging, Speech Recognition

Code-switching is the fluent alternation between two or more languages in conversation between bilinguals. Large populations of speakers code-switch during communication, but little effort has been made to develop tools for code-switching, including part-of-speech taggers. In this paper, we propose an approach to POS tagging of code-switched English-Spanish data based on recurrent neural networks. We test our model on known monolingual benchmarks to demonstrate that our neural POS tagging model is on par with state-of-the-art methods. We next test our code-switched methods on the Miami Bangor corpus of English Spanish conversation, focusing on two types of experiments: POS tagging alone, for which we achieve 96.34{\%} accuracy, and joint part-of-speech and language ID tagging, which achieves similar POS tagging accuracy (96.39{\%}) and very high language ID accuracy (98.78{\%}). Finally, we show that our proposed models outperform other state-of-the-art code-switched taggers.
Phone Merging For Code-Switched Speech Recognition
Authors: Sunit Sivasankaran, Brij Mohan Lal Srivastava, Sunayana Sitaram, Kalika Bali, Monojit Choudhury
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Speakers in multilingual communities often switch between or mix multiple languages in the same conversation. Automatic Speech Recognition (ASR) of code-switched speech faces many challenges including the influence of phones of different languages on each other. This paper shows evidence that phone sharing between languages improves the Acoustic Model performance for Hindi-English code-switched speech. We compare baseline system built with separate phones for Hindi and English with systems where the phones were manually merged based on linguistic knowledge. Encouraged by the improved ASR performance after manually merging the phones, we further investigate multiple data-driven methods to identify phones to be merged across the languages. We show detailed analysis of automatic phone merging in this language pair and the impact it has on individual phone accuracies and WER. Though the best performance gain of 1.2{\%} WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Improving Neural Network Performance by Injecting Background Knowledge: Detecting Code-switching and Borrowing in Algerian texts
Authors: Wafia Adouane, Jean-Philippe Bernardy, Simon Dobnik
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Word Embeddings

We explore the effect of injecting background knowledge to different deep neural network (DNN) configurations in order to mitigate the problem of the scarcity of annotated data when applying these models on datasets of low-resourced languages. The background knowledge is encoded in the form of lexicons and pre-trained sub-word embeddings. The DNN models are evaluated on the task of detecting code-switching and borrowing points in non-standardised user-generated Algerian texts. Overall results show that DNNs benefit from adding background knowledge. However, the gain varies between models and categories. The proposed DNN architectures are generic and could be applied to other low-resourced languages.
Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques
Authors: Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Question Answering, Sentence

Code-Mixing (CM) is the phenomenon of alternating between two or more languages which is prevalent in bi- and multi-lingual communities. Most NLP applications today are still designed with the assumption of a single interaction language and are most likely to break given a CM utterance with multiple languages mixed at a morphological, phrase or sentence level. For example, popular commercial search engines do not yet fully understand the intents expressed in CM queries. As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian). We share the details of our data collection process, techniques which were used to avoid inducing lexical bias amongst the crowd workers and other CM specific linguistic properties of the dataset. Our final dataset, which is available freely for research purposes, has 1,694 Hinglish, 2,848 Tamlish and 1,391 Tenglish factoid questions and their answers. We discuss the techniques used by the participants for the first edition of this ongoing challenge.
Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base
Authors: Vishal Gupta, Manoj Chinnakotla, Manish Shrivastava
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition (ASR), Information Retrieval, Language Identification, Machine Translation, Part-Of-Speech Tagging, Question Answering, Reading Comprehension, Re-Ranking, Sentence, Speech Recognition, Translation, Transliteration, Triplet

Humans can learn multiple languages. If they know a fact in one language, they can answer a question in another language they understand. They can also answer Code-mix (CM) questions: questions which contain both languages. This behavior is attributed to the unique learning ability of humans. Our task aims to study if machines can achieve this. We demonstrate how effectively a machine can answer CM questions. In this work, we adopt a two phase approach: candidate generation and candidate re-ranking to answer questions. We propose a Triplet-Siamese-Hybrid CNN (TSHCNN) to re-rank candidate answers. We show experiments on the SimpleQuestions dataset. Our network is trained only on English questions provided in this dataset and noisy Hindi translations of these questions and can answer English-Hindi CM questions effectively without the need of translation into English. Back-transliterated CM questions outperform their lexical and sentence level translated counterparts by 5{\%} {\&} 35{\%} in accuracy respectively, highlighting the efficacy of our approach in a resource constrained setting.
Language Identification and Analysis of Code-Switched Social Media Text
Authors: Deepthi Mave, Suraj Maharjan, Thamar Solorio
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Identification, Machine Translation

In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset. In this regard, we build a new code-switched dataset for Hindi-English. To understand the code-switching patterns in these language pairs, we investigate different code-switching metrics. We find that the CRF model outperforms the neural network based models by a margin of 2-5 percentage points for Spanish-English and 3-5 percentage points for Hindi-English.
Predicting the presence of a Matrix Language in code-switching
Authors: Barbara Bullock, Wally Guzm{\'a}n, Jacqueline Serigos, Vivek Sharath, Almeida Jacqueline Toribio
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Sentence

One language is often assumed to be dominant in code-switching but this assumption has not been empirically tested. We operationalize the matrix language (ML) at the level of the sentence, using three common definitions from linguistics. We test whether these converge and then model this convergence via a set of metrics that together quantify the nature of C-S. We conduct our experiment on four Spanish-English corpora. Our results demonstrate that our model can separate some corpora according to whether they have a dominant ML or not but that the corpora span a range of mixing types that cannot be sorted neatly into an insertional vs. alternational dichotomy.
Automatic Detection of Code-switching Style from Acoustics
Authors: Rallab, SaiKrishna i, Sunayana Sitaram, Alan W. black
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition (ASR), Language Identification, speech-recognition, Speech Recognition

Multilingual speakers switch between languages in an non-trivial fashion displaying inter sentential, intra sentential, and congruent lexicalization based transitions. While monolingual ASR systems may be capable of recognizing a few words from a foreign language, they are usually not robust enough to handle these varied styles of code-switching. There is also a lack of large code-switched speech corpora capturing all these styles making it difficult to build code-switched speech recognition systems. We hypothesize that it may be useful for an ASR system to be able to first detect the switching style of a particular utterance from acoustics, and then use specialized language models or other adaptation techniques for decoding the speech. In this paper, we look at the first problem of detecting code-switching style from acoustics. We classify code-switched Spanish-English and Hindi-English corpora using two metrics and show that features extracted from acoustics alone can distinguish between different kinds of code-switching in these language pairs.
Accommodation of Conversational Code-Choice
Authors: Anshul Bawa, Monojit Choudhury, Kalika Bali
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, Retrieval

Bilingual speakers often freely mix languages. However, in such bilingual conversations, are the language choices of the speakers coordinated? How much does one speaker{'}s choice of language affect other speakers? In this paper, we formulate code-choice as a linguistic style, and show that speakers are indeed sensitive to and accommodating of each other{'}s code-choice. We find that the saliency or markedness of a language in context directly affects the degree of accommodation observed. More importantly, we discover that accommodation of code-choices persists over several conversational turns. We also propose an alternative interpretation of conversational accommodation as a retrieval problem, and show that the differences in accommodation characteristics of code-choices are based on their markedness in context.
Language Informed Modeling of Code-Switched Text
Authors: Ch, Khyathi u, Thomas Manzini, Sumeet Singh, Alan W. black
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling, Machine Translation, Speech Recognition

Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities. CS texts have a complex interplay between languages and occur in informal contexts that make them harder to collect and construct NLP tools for. We approach this problem through Language Modeling (LM) on a new Hindi-English mixed corpus containing 59,189 unique sentences collected from blogging websites. We implement and discuss different Language Models derived from a multi-layered LSTM architecture. We hypothesize that encoding language information strengthens a language model by helping to learn code-switching points. We show that our highest performing model achieves a test perplexity of 19.52 on the CS corpus that we collected and processed. On this data we demonstrate that our performance is an improvement over AWD-LSTM LM (a recent state of the art on monolingual English).
GHHT at CALCS 2018: Named Entity Recognition for Dialectal Arabic Using Neural Networks
Authors: Mohammed Attia, Younes Samih, Wolfgang Maier
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes our system submission to the CALCS 2018 shared task on named entity recognition on code-switched data for the language variant pair of Modern Standard Arabic and Egyptian dialectal Arabic. We build a a Deep Neural Network that combines word and character-based representations in convolutional and recurrent networks with a CRF layer. The model is augmented with stacked layers of enriched information such pre-trained embeddings, Brown clusters and named entity gazetteers. Our system is ranked second among those participating in the shared task achieving an FB1 average of 70.09{\%}.
Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data
Authors: Devanshu Jain, Maria Kustikova, Mayank Darbari, Rishabh Gupta, Stephen Mayhew
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Single Particle Analysis, Translation, Transliteration

In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL{'}18. Code-switching is the phenomenon where a speaker switches between two languages or variants of the same language within or across utterances, known as intra-sentential or inter-sentential code-switching, respectively. Processing such data is challenging using state of the art methods since such technology is generally geared towards processing monolingual text. In this paper we explored ways to use language identification and translation to recognize named entities in such data, however, utilizing simple features (sans multi-lingual features) with Conditional Random Field (CRF) classifier achieved the best results. Our experiments were mainly aimed at the (ENG-SPA) English-Spanish dataset but we submitted a language-independent version of our system to the (MSA-EGY) Arabic-Egyptian dataset as well and achieved good results.
Named Entity Recognition on Code-Switched Data Using Conditional Random Fields
Authors: Utpal Kumar Sikdar, Biswanath Barik, Bj{\"o}rn Gamb{\"a}ck
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Named Entity Recognition is an important information extraction task that identifies proper names in unstructured texts and classifies them into some pre-defined categories. Identification of named entities in code-mixed social media texts is a more difficult and challenging task as the contexts are short, ambiguous and often noisy. This work proposes a Conditional Random Fields based named entity recognition system to identify proper names in code-switched data and classify them into nine categories. The system ranked fifth among nine participant systems and achieved a 59.25{\%} F1-score.
The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018
Authors: Florian Janke, Tongrui Li, Eric Rinc{\'o}n, Gualberto Guzm{\'a}n, Barbara Bullock, Almeida Jacqueline Toribio
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Single Particle Analysis

This paper describes the system for the Named Entity Recognition Shared Task of the Third Workshop on Computational Approaches to Linguistic Code-Switching (CALCS) submitted by the Bilingual Annotations Tasks (BATs) research group of the University of Texas. Our system uses several features to train a Conditional Random Field (CRF) model for classifying input words as Named Entities (NEs) using the Inside-Outside-Beginning (IOB) tagging scheme. We participated in the Modern Standard Arabic-Egyptian Arabic (MSA-EGY) and English-Spanish (ENG-SPA) tasks, achieving weighted average F-scores of 65.62 and 54.16 respectively. We also describe the performance of a deep neural network (NN) trained on a subset of the CRF features, which did not surpass CRF performance.