pwc-archive/papers-with-abstracts huggingface dataset:
Tackling Code-Switched NER: Participation of CMU
Authors: Parvathy Geetha, Ch, Khyathi u, Alan W. black
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering

Named Entity Recognition plays a major role in several downstream applications in NLP. Though this task has been heavily studied in formal monolingual texts and also noisy texts like Twitter data, it is still an emerging task in code-switched (CS) content on social media. This paper describes our participation in the shared task of NER on code-switched data for Spanglish (Spanish + English) and Arabish (Arabic + English). In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data. Owing to the sparse and non-linear relationships between words in Twitter data, we explored neural architectures that are capable of non-linearities fairly well. In specific, we trained character level models and word level models based on Bidirectional LSTMs (Bi-LSTMs) to perform sequential tagging. We train multiple models to identify nominal mentions and subsequently use this information to predict the labels of named entity in a sequence. Our best model is a character level model along with word level pre-trained multilingual embeddings that gave an F-score of 56.72 in Spanglish and a word level model that gave an F-score of 65.02 in Arabish on the test data.
Multilingual Named Entity Recognition on Spanish-English Code-switched Tweets using Support Vector Machines
Authors: Daniel Claeser, Samantha Kent, Dennis Felske
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Entity Linking, Multilingual Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing.
IIT (BHU) Submission for the ACL Shared Task on Named Entity Recognition on Code-switched Data
Authors: Shashwat Trivedi, Harsh Rangwani, Anil Kumar Singh
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Single Particle Analysis

This paper describes the best performing system for the shared task on Named Entity Recognition (NER) on code-switched data for the language pair Spanish-English (ENG-SPA). We introduce a gated neural architecture for the NER task. Our final model achieves an F1 score of 63.76{\%}, outperforming the baseline by 10{\%}.
Code-Switched Named Entity Recognition with Embedding Attention
Authors: Changhan Wang, Kyunghyun Cho, Douwe Kiela
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

We describe our work for the CALCS 2018 shared task on named entity recognition on code-switched data. Our system ranked first place for MS Arabic-Egyptian named entity recognition and third place for English-Spanish.
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion
Authors: Jennifer Williams, Steven Kleinegesse, Ramona Comanescu, Oana Radu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Recognition, Machine Translation

We present our system description of input-level multimodal fusion of audio, video, and text for recognition of emotions and their intensities for the 2018 First Grand Challenge on Computational Modeling of Human Multimodal Language. Our proposed approach is based on input-level feature fusion with sequence learning from Bidirectional Long-Short Term Memory (BLSTM) deep neural networks (DNNs). We show that our fusion approach outperforms unimodal predictors. Our system performs 6-way simultaneous classification and regression, allowing for overlapping emotion labels in a video segment. This leads to an overall binary accuracy of 90{\%}, overall 4-class accuracy of 89.2{\%} and an overall mean-absolute-error (MAE) of 0.12. Our work shows that an early fusion technique can effectively predict the presence of multi-label emotions as well as their coarse-grained intensities. The presented multimodal approach creates a simple and robust baseline on this new Grand Challenge dataset. Furthermore, we provide a detailed analysis of emotion intensity distributions as output from our DNN, as well as a related discussion concerning the inherent difficulty of this task.
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities
Authors: Imran Sheikh, Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Automatic Speech Recognition (ASR), General Classification, Sentiment Analysis, Sentiment Classification, Speech Recognition

Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views. This work focuses on improving the representations of these views by performing a deep canonical correlation analysis with the representations of the better performing manual transcription view. Enhanced representations of the imperfect views can be obtained even in absence of the perfect views and give an improved performance during test conditions. Evaluations on the CMU-MOSI and CMU-MOSEI datasets demonstrate the effectiveness of the proposed approach.
DNN Multimodal Fusion Techniques for Predicting Video Sentiment
Authors: Jennifer Williams, Ramona Comanescu, Oana Radu, Leimin Tian
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: feature selection, Multimodal Sentiment Analysis, Sentiment Analysis

We present our work on sentiment prediction using the benchmark MOSI dataset from the CMU-MultimodalDataSDK. Previous work on multimodal sentiment analysis have been focused on input-level feature fusion or decision-level fusion for multimodal fusion. Here, we propose an intermediate-level feature fusion, which merges weights from each modality (audio, video, and text) during training with subsequent additional training. Moreover, we tested principle component analysis (PCA) for feature selection. We found that applying PCA increases unimodal performance, and multimodal fusion outperforms unimodal models. Our experiments show that our proposed intermediate-level feature fusion outperforms other fusion techniques, and it achieves the best performance with an overall binary accuracy of 74.0{\%} on video+text modalities. Our work also improves feature selection for unimodal sentiment analysis, while proposing a novel and effective multimodal fusion architecture for this task.
Character-level Supervision for Low-resource POS Tagging
Authors: Katharina Kann, Johannes Bjerva, Isabelle Augenstein, Barbara Plank, Anders S{\o}gaard
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Feature Engineering, LEMMA, Lemmatization, Multi-Task Learning, POS, POS Tagging

Neural part-of-speech (POS) taggers are known to not perform well with little training data. As a step towards overcoming this problem, we present an architecture for learning more robust neural POS taggers by jointly training a hierarchical, recurrent model and a recurrent character-based sequence-to-sequence network supervised using an auxiliary objective. This way, we introduce stronger character-level supervision into the model, which enables better generalization to unseen words and provides regularization, making our encoding less prone to overfitting. We experiment with three auxiliary tasks: lemmatization, character-based word autoencoding, and character-based random string autoencoding. Experiments with minimal amounts of labeled data on 34 languages show that our new architecture outperforms a single-task baseline and, surprisingly, that, on average, raw text autoencoding can be as beneficial for low-resource POS tagging as using lemma information. Our neural POS tagger closes the gap to a state-of-the-art POS tagger (MarMoT) for low-resource scenarios by 43{\%}, even outperforming it on languages with templatic morphology, e.g., Arabic, Hebrew, and Turkish, by some margin.
Multi-task learning for historical text normalization: Size matters
Authors: Marcel Bollmann, Anders S{\o}gaard, Joachim Bingel
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Grammatical Error Correction, Multi-Task Learning, Text Generation, Text Normalization

Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding{---}contrary to what has been observed for other NLP tasks{---}is that multi-task learning mainly works when target task data is very scarce.
Compositional Language Modeling for Icon-Based Augmentative and Alternative Communication
Authors: Shiran Dudy, Steven Bedrick
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling, Spoken Language Understanding

Icon-based communication systems are widely used in the field of Augmentative and Alternative Communication. Typically, icon-based systems have lagged behind word- and character-based systems in terms of predictive typing functionality, due to the challenges inherent to training icon-based language models. We propose a method for synthesizing training data for use in icon-based language models, and explore two different modeling strategies. We propose a method to generate language models for corpus-less symbol-set.
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data
Authors: Koel Dutta Chowdhury, Mohammed Hasanuzzaman, Qun Liu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Image Description, Machine Translation, Question Answering, Translation, Video Description, Visual Question Answering (VQA)

In this paper, we investigate the effectiveness of training a multimodal neural machine translation (MNMT) system with image features for a low-resource language pair, Hindi and English, using synthetic data. A three-way parallel corpus which contains bilingual texts and corresponding images is required to train a MNMT system with image features. However, such a corpus is not available for low resource language pairs. To address this, we developed both a synthetic training dataset and a manually curated development/test dataset for Hindi based on an existing English-image parallel corpus. We used these datasets to build our image description translation system by adopting state-of-the-art MNMT models. Our results show that it is possible to train a MNMT system for low-resource language pairs through the use of synthetic data and that such a system can benefit from image features.
Investigating Effective Parameters for Fine-tuning of Word Embeddings Using Only a Small Corpus
Authors: Kanako Komiya, Hiroyuki Shinnou
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling, Word Embeddings

Fine-tuning is a popular method to achieve better performance when only a small target corpus is available. However, it requires tuning of a number of metaparameters and thus it might carry risk of adverse effect when inappropriate metaparameters are used. Therefore, we investigate effective parameters for fine-tuning when only a small target corpus is available. In the current study, we target at improving Japanese word embeddings created from a huge corpus. First, we demonstrate that even the word embeddings created from the huge corpus are affected by domain shift. After that, we investigate effective parameters for fine-tuning of the word embeddings using a small target corpus. We used perplexity of a language model obtained from a Long Short-Term Memory network to assess the word embeddings input into the network. The experiments revealed that fine-tuning sometimes give adverse effect when only a small target corpus is used and batch size is the most important parameter for fine-tuning. In addition, we confirmed that effect of fine-tuning is higher when size of a target corpus was larger.
Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification
Authors: Mingkuan Liu, Musen Wen, Selcuk Kopru, Xianjing Liu, Alan Lu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Classification, General Classification, Machine Translation, text-classification, Text Classification

The lack of high-quality labeled training data has been one of the critical challenges facing many industrial machine learning tasks. To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models. The method employs a primary model Main and an auxiliary evaluation model Eval, where Main and Eval models are trained iteratively by automatically generating labeled data from unlabeled data and/or users{'} feedback signals. The proposed approach is applied to different text classification tasks. We report results on both the publicly available Yahoo! Answers dataset and our e-commerce product classification dataset. The experimental results show that the proposed method reduces the classification error rate by 4{\%} and up to 15{\%} across various experimental setups and datasets. A detailed comparison with other semi-supervised learning approaches is also presented later in the paper. The results from various text classification tasks demonstrate that our method outperforms those developed in previous related studies.
Sociolinguistic Corpus of WhatsApp Chats in Spanish among College Students
Authors: Alej Dorantes, ro, Gerardo Sierra, Tlauhlia Yam{\'\i}n Donohue P{\'e}rez, Gemma Bel-Enguix, M{\'o}nica Jasso Rosales
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

This work presents the Sociolinguistic Corpus of WhatsApp Chats in Spanish among College Students, a corpus of raw data for general use. Its purpose is to offer data for the study of of language and interactions via Instant Messaging (IM) among bachelors. Our paper consists of an overview of both the corpus{'}s content and demographic metadata. Furthermore, it presents the current research being conducted with it {---}namely parenthetical expressions, orality traits, and code-switching. This work also includes a brief outline of similar corpora and recent studies in the field of IM.
A Twitter Corpus for Hindi-English Code Mixed POS Tagging
Authors: Kushagra Singh, Indira Sen, Ponnurangam Kumaraguru
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: POS, POS Tagging

Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies. Code-mixed content on social media is also on the rise, prompting the need for tools to automatically understand such content. Automatic Parts-of-Speech (POS) tagging is an essential step in any Natural Language Processing (NLP) pipeline, but there is a lack of annotated data to train such models. In this work, we present a unique language tagged and POS-tagged dataset of code-mixed English-Hindi tweets related to five incidents in India that led to a lot of Twitter activity. Our dataset is unique in two dimensions: (i) it is larger than previous annotated datasets and (ii) it closely resembles typical real-world tweets. Additionally, we present a POS tagging model that is trained on this dataset to provide an example of how this dataset can be used. The model also shows the efficacy of our dataset in enabling the creation of code-mixed social media POS taggers.
Detecting Offensive Tweets in Hindi-English Code-Switched Language
Authors: Puneet Mathur, Rajiv Shah, Ramit Sawhney, Debanjan Mahata
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Hate Speech Detection, Transfer Learning

The exponential rise of social media websites like Twitter, Facebook and Reddit in linguistically diverse geographical regions has led to hybridization of popular native languages with English in an effort to ease communication. The paper focuses on the classification of offensive tweets written in Hinglish language, which is a portmanteau of the Indic language Hindi with the Roman script. The paper introduces a novel tweet dataset, titled Hindi-English Offensive Tweet (HEOT) dataset, consisting of tweets in Hindi-English code switched language split into three classes: non-offensive, abusive and hate-speech. Further, we approach the problem of classification of the tweets in HEOT dataset using transfer learning wherein the proposed model employing Convolutional Neural Networks is pre-trained on tweets in English followed by retraining on Hinglish tweets.
SocialNLP 2018 EmotionX Challenge Overview: Recognizing Emotions in Dialogues
Authors: Chao-Chun Hsu, Lun-Wei Ku
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Common Sense Reasoning, Emotion Recognition

This paper describes an overview of the Dialogue Emotion Recognition Challenge, EmotionX, at the Sixth SocialNLP Workshop, which recognizes the emotion of each utterance in dialogues. This challenge offers the EmotionLines dataset as the experimental materials. The EmotionLines dataset contains conversations from Friends TV show transcripts (Friends) and real chatting logs (EmotionPush), where every dialogue utterance is labeled with emotions. Organizers provide baseline results. 18 teams registered in this challenge and 5 of them submitted their results successfully. The best team achieves the unweighted accuracy 62.48 and 62.5 on EmotionPush and Friends, respectively. In this paper we present the task definition, test collection, the evaluation results of the groups that participated in this challenge, and their approach.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
Authors: Linkai Luo, Haiqin Yang, Francis Y. L. Chin
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Classification, General Classification, Sentence Classification, Sentence Embedding

In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the Friends and the EmotionPush test sets, respectively.
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier
Authors: Sopan Khosla
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Classification, Emotion Recognition, Sentiment Analysis

In this paper, we model emotions in EmotionLines dataset using a convolutional-deconvolutional autoencoder (CNN-DCNN) framework. We show that adding a joint reconstruction loss improves performance. Quantitative evaluation with jointly trained network, augmented with linguistic features, reports best accuracies for emotion prediction; namely joy, sadness, anger, and neutral emotion in text.
EmotionX-SmartDubai\_NLP: Detecting User Emotions In Social Media Text
Authors: Hessa AlBalooshi, Shahram Rahmanian, Rahul Venkatesh Kumar
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Feature Engineering, General Classification, Natural Language Understanding, text-classification, Text Classification, Word Sense Disambiguation

This paper describes the working note on {``}EmotionX{''} shared task. It is hosted by SocialNLP 2018. The objective of this task is to detect the emotions, based on each speaker{'}s utterances that are in English. Taking this as multiclass text classification problem, we have experimented to develop a model to classify the target class. The primary challenge in this task is to detect the emotions in short messages, communicated through social media. This paper describes the participation of SmartDubai{\_}NLP team in EmotionX shared task and our investigation to detect the emotions from utterance using Neural networks and Natural language understanding.
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling
Authors: Rohit Saxena, Savita Bhat, Niranjan Pedanekar
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Classification, Emotion Recognition, Word Embeddings

This paper presents our system submitted to the EmotionX challenge. It is an emotion detection task on dialogues in the EmotionLines dataset. We formulate this as a hierarchical network where network learns data representation at both utterance level and dialogue level. Our model is inspired by Hierarchical Attention network (HAN) and uses pre-trained word embeddings as features. We formulate emotion detection in dialogues as a sequence labeling problem to capture the dependencies among labels. We report the performance accuracy for four emotions (anger, joy, neutral and sadness). The model achieved unweighted accuracy of 55.38{\%} on Friends test dataset and 56.73{\%} on EmotionPush test dataset. We report an improvement of 22.51{\%} in Friends dataset and 36.04{\%} in EmotionPush dataset over baseline results.
EmotionX-JTML: Detecting emotions with Attention
Authors: Johnny Torres
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Emotion Classification, Representation Learning

This paper addresses the problem of automatic recognition of emotions in conversational text datasets for the EmotionX challenge. Emotion is a human characteristic expressed through several modalities (e.g., auditory, visual, tactile). Trying to detect emotions only from the text becomes a difficult task even for humans. This paper evaluates several neural architectures based on Attention Models, which allow extracting relevant parts of the context within a conversation to identify the emotion associated with each utterance. Empirical results in the validation datasets demonstrate the effectiveness of the approach compared to the reference models for some instances, and other cases show better results with simpler models.
Towards Automation of Sense-type Identification of Verbs in OntoSenseNet
Authors: Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Vocal Bursts Type Prediction

In this paper, we discuss the enrichment of a manually developed resource, OntoSenseNet for Telugu. OntoSenseNet is a sense annotated resource that marks each verb of Telugu with a primary and a secondary sense. The area of research is relatively recent but has a large scope of development. We provide an introductory work to enrich the OntoSenseNet to promote further research in Telugu. Classifiers are adopted to learn the sense relevant features of the words in the resource and also to automate the tagging of sense-types for verbs. We perform a comparative analysis of different classifiers applied on OntoSenseNet. The results of the experiment prove that automated enrichment of the resource is effective using SVM classifiers and Adaboost ensemble.