pwc-archive/papers-with-abstracts huggingface dataset:
Modelling Natural Language, Programs, and their Intersection
Authors: Graham Neubig, Miltiadis Allamanis
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Parsing, Text Generation

As computers and information grow a more integral part of our world, it is becoming more and more important for humans to be able to interact with their computers in complex ways. One way to do so is by programming, but the ability to understand and generate programming languages is a highly specialized skill. As a result, in the past several years there has been an increasing research interest in methods that focus on the intersection of programming and natural language, allowing users to use natural language to interact with computers in the complex ways that programs allow us to do. In this tutorial, we will focus on machine learning models of programs and natural language focused on making this goal a reality. First, we will discuss the similarities and differences between programming and natural language. Then we will discuss methods that have been designed to cover a variety of tasks in this field, including automatic explanation of programs in natural language (code-to-language), automatic generation of programs from natural language specifications (language-to-code), modeling the natural language elements of source code, and analysis of communication in collaborative programming communities. The tutorial will be aimed at NLP researchers and practitioners, aiming to describe the interesting opportunities that models at the intersection of natural and programming languages provide, and also how their techniques could provide benefit to the practice of software engineering as a whole.
Deep Learning Approaches to Text Production
Authors: Claire Gardent, Shashi Narayan
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data-to-Text Generation, Decoder, Deep Learning, Machine Translation, Sentence, Sentence Compression, Sentence Fusion, Text Generation, Text Simplification

Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves, to verbalise the content of Knowledge bases or to generate natural English sentences from rich linguistic representations, such as dependency trees or Abstract Meaning Representations. In text-driven methods on the other hand, text production is at work in sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, text summarisation and end-to-end dialogue systems. Following the success of encoder-decoder models in modeling sequence-rewriting tasks such as machine translation, deep learning models have successfully been applied to the various text production tasks. In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural models for text production. Each text production task raises a slightly different communication goal (e.g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation). We will outline the constraints specific to each subtasks and examine how the existing neural models account for them.
Scalable Construction and Reasoning of Massive Knowledge Bases
Authors: Xiang Ren, Nanyun Peng, William Yang Wang
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Articles, Deep Reinforcement Learning, Knowledge Base Construction, Reinforcement Learning

In today{'}s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), which grows at an astonishing rate. Yet this knowledge is mostly inaccessible to computers and overwhelming for human experts to absorb. How to turn such massive and unstructured text data into structured, actionable knowledge, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community. Traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In the first part of the tutorial, we introduce how to extract structured facts (i.e., entities and their relations for types of interest) from text corpora to construct knowledge bases, with a focus on methods that are weakly-supervised and domain-independent for timely knowledge base construction across various application domains. In the second part, we introduce how to leverage other knowledge, such as the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct low-resource information extraction. In the third part, we describe recent advances in knowledge base reasoning. We start with the gentle introduction to the literature, focusing on path-based and embedding based methods. We then describe DeepPath, a recent attempt of using deep reinforcement learning to combine the best of both worlds for knowledge base reasoning.
Socially Responsible NLP
Authors: Yulia Tsvetkov, Vinodkumar Prabhakaran, Rob Voigt
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decision Making, Ethics

As language technologies have become increasingly prevalent, there is a growing awareness that decisions we make about our data, methods, and tools are often tied up with their impact on people and societies. This tutorial will provide an overview of real-world applications of language technologies and the potential ethical implications associated with them. We will discuss philosophical foundations of ethical research along with state of the art techniques. Through this tutorial, we intend to provide the NLP researcher with an overview of tools to ensure that the data, algorithms, and models that they build are socially responsible. These tools will include a checklist of common pitfalls that one should avoid (e.g., demographic bias in data collection), as well as methods to adequately mitigate these issues (e.g., adjusting sampling rates or de-biasing through regularization). The tutorial is based on a new course on Ethics and NLP developed at Carnegie Mellon University.
Deep Learning for Conversational AI
Authors: Pei-Hao Su, Nikola Mrk{\v{s}}i{\'c}, I{\~n}igo Casanueva, Ivan Vuli{\'c}
Date: 2018-06-01 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decision Making, Deep Learning, Dialogue Management, Dialogue State Tracking, Goal-Oriented Dialogue Systems, Management, Spoken Dialogue Systems, Text Generation

Spoken Dialogue Systems (SDS) have great commercial potential as they promise to revolutionise the way in which humans interact with machines. The advent of deep learning led to substantial developments in this area of NLP research, and the goal of this tutorial is to familiarise the research community with the recent advances in what some call the most difficult problem in NLP. From a research perspective, the design of spoken dialogue systems provides a number of significant challenges, as these systems depend on: a) solving several difficult NLP and decision-making tasks; and b) combining these into a functional dialogue system pipeline. A key long-term goal of dialogue system research is to enable open-domain systems that can converse about arbitrary topics and assist humans with completing a wide range of tasks. Furthermore, such systems need to autonomously learn on-line to improve their performance and recover from errors using both signals from their environment and from implicit and explicit user feedback. While the design of such systems has traditionally been modular, domain and language-specific, advances in deep learning have alleviated many of the design problems. The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems. We plan to introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems, which includes three key components: 1) Language Understanding; 2) Dialogue Management; and 3) Language Generation. The differences between goal-oriented dialogue systems and chat-bot style conversational agents will be explained in order to show the motivation behind the design of both, with the main focus on the pipeline SDS framework. For each key component, we will define the research problem, provide a brief literature review and introduce the current state-of-the-art approaches. Complementary resources (e.g. available datasets and toolkits) will also be discussed. Finally, future work, outstanding challenges, and current industry practices will be presented. All of the presented material will be made available online for future reference.
SemEval-2018 Task 1: Affect in Tweets
Authors: Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, Svetlana Kiritchenko
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Emotion Classification, General Classification, Ordinal Classification, regression

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.
SemEval 2018 Task 2: Multilingual Emoji Prediction
Authors: Francesco Barbieri, Jose Camacho-Collados, Francesco Ronzano, Luis Espinosa-Anke, Miguel Ballesteros, Valerio Basile, Viviana Patti, Horacio Saggion
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Recognition, Information Retrieval, Prediction, Sentiment Analysis, Task 2

This paper describes the results of the first Shared Task on Multilingual Emoji Prediction, organized as part of SemEval 2018. Given the text of a tweet, the task consists of predicting the most likely emoji to be used along such tweet. Two subtasks were proposed, one for English and one for Spanish, and participants were allowed to submit a system run to one or both subtasks. In total, 49 teams participated to the English subtask and 22 teams submitted a system run to the Spanish subtask. Evaluation was carried out emoji-wise, and the final ranking was based on macro F-Score. Data and further information about this task can be found at \url{https://competitions.codalab.org/competitions/17344}.
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Document Classification, General Classification, Hyperparameter Optimization, Language Identification, Sentiment Analysis, Task 2, Text Classification, Word Embeddings

This paper describes our participation in the SemEval-2018 task Multilingual Emoji Prediction. We participated in both English and Spanish subtasks, experimenting with support vector machines (SVMs) and recurrent neural networks. Our SVM classifier obtained the top rank in both subtasks with macro-averaged F1-measures of 35.99{\%} for English and 22.36{\%} for Spanish data sets. Similar to a few earlier attempts, the results with neural networks were not on par with linear SVMs.
SemEval-2018 Task 3: Irony Detection in English Tweets
Authors: Cynthia Van Hee, Els Lefever, V{\'e}ronique Hoste
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Binary Classification, Classification, General Classification, Sentiment Analysis

This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. {\#}irony, {\#}sarcasm, {\#}not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.
THU\_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning
Authors: Chuhan Wu, Fangzhao Wu, Sixing Wu, Junxin Liu, Zhigang Yuan, Yongfeng Huang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Feature Engineering, Multi-Task Learning, Sentiment Analysis

Detecting irony is an important task to mine fine-grained information from social web messages. Therefore, the Semeval-2018 task 3 is aimed to detect the ironic tweets (subtask A) and their ironic types (subtask B). In order to address this task, we propose a system based on a densely connected LSTM network with multi-task learning strategy. In our dense LSTM model, each layer will take all outputs from previous layers as input. The last LSTM layer will output the hidden representations of texts, and they will be used in three classification task. In addition, we incorporate several types of features to improve the model performance. Our model achieved an F-score of 70.54 (ranked 2/43) in the subtask A and 49.47 (ranked 3/29) in the subtask B. The experimental results validate the effectiveness of our system.
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues
Authors: Jinho D. Choi, Henry Y. Chen
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Entity Linking, Machine Translation, Question Answering

Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue. For this task, the first two seasons of the popular TV show Friends are annotated, comprising a total of 448 dialogues, 15,709 mentions, and 401 entities. The personal mentions are detected from nominals referring to certain characters in the show, and the entities are collected from the list of all characters in those two seasons of the show. This task is challenging because it requires the identification of characters that are mentioned but may not be active during the conversation. Among 90+ participants, four of them submitted their system outputs and showed strengths in different aspects about the task. Thorough analyses of the distributed datasets, system outputs, and comparative studies are also provided. To facilitate the momentum, we create an open-source project for this task and publicly release a larger and cleaner dataset, hoping to support researchers for more enhanced modeling.
SemEval-2018 Task 5: Counting Events and Participants in the Long Tail
Authors: Marten Postma, Filip Ilievski, Piek Vossen
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Word Sense Disambiguation

This paper discusses SemEval-2018 Task 5: a referential quantification task of counting events and participants in local, long-tail news documents with high ambiguity. The complexity of this task challenges systems to establish the meaning, reference and identity across documents. The task consists of three subtasks and spans across three domains. We detail the design of this referential quantification task, describe the participating systems, and present additional analysis to gain deeper insight into their performance.
KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents
Authors: Paramita Mirza, Fariz Darari, Rahmad Mahendra
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Articles, Clustering, coreference-resolution, Coreference Resolution, Event Extraction, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Semantic Role Labeling, Word Sense Disambiguation

We present KOI (Knowledge of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events. KOI-KG can then be used to efficiently answer questions such {``}How many killing incidents happened in 2017 that involve Sean?{''} The required steps in building the KG include: (i) document preprocessing involving word sense disambiguation, named-entity recognition, temporal expression recognition and normalization, and semantic role labeling; (ii) incidental event extraction and coreference resolution via document clustering; and (iii) KG construction and population.
SemEval 2018 Task 6: Parsing Time Normalizations
Authors: Egoitz Laparra, Dongfang Xu, Ahmed Elsayed, Steven Bethard, Martha Palmer
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Semantic Parsing

This paper presents the outcomes of the Parsing Time Normalization shared task held within SemEval-2018. The aim of the task is to parse time expressions into the compositional semantic graphs of the Semantically Compositional Annotation of Time Expressions (SCATE) schema, which allows the representation of a wider variety of time expressions than previous approaches. Two tracks were included, one to evaluate the parsing of individual components of the produced graphs, in a classic information extraction way, and another one to evaluate the quality of the time intervals resulting from the interpretation of those graphs. Though 40 participants registered for the task, only one team submitted output, achieving 0.55 F1 in Track 1 (parsing) and 0.70 F1 in Track 2 (intervals).
Chrono at SemEval-2018 Task 6: A System for Normalizing Temporal Expressions
Authors: Amy Olex, Luke Maffey, Nicholas Morgan, Bridget McInnes
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Temporal Information Extraction, Timex normalization

Temporal information extraction is a challenging task. Here we describe Chrono, a hybrid rule-based and machine learning system that identifies temporal expressions in text and normalizes them into the SCATE schema. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 Task 6: Parsing Time Normalizations.
NEUROSENT-PDI at SemEval-2018 Task 1: Leveraging a Multi-Domain Sentiment Model for Inferring Polarity in Micro-blog Text
Authors: Mauro Dragoni
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis, Word Embeddings

This paper describes the NeuroSent system that participated in SemEval 2018 Task 1. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis
Authors: Maja Karasalo, Mattias Nilsson, Magnus Rosell, Ulrika Wickenberg Bolin
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, General Classification, Transfer Learning

This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95{\%} of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination
Authors: Zhengxin Zhang, Qimin Zhou, Hao Wu
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

In this paper, we put forward a system that competed at SemEval-2018 Task 1: {``}Affect in Tweets{''}. Our system uses a simple yet effective ensemble method which combines several neural network components. We participate in two subtasks for English tweets: EI-reg and V-reg. For two subtasks, different combinations of neural components are examined. For EI-reg, our system achieves an accuracy of 0.727 in Pearson Correlation Coefficient (all instances) and an accuracy of 0.555 in Pearson Correlation Coefficient (0.5-1). For V-reg, the achieved accuracy scores are respectively 0.835 and 0.670
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets
Authors: Luna De Bruyne, Orph{\'e}e De Clercq, V{\'e}ronique Hoste
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, General Classification, Sentiment Analysis

This paper presents an emotion classification system for English tweets, submitted for the SemEval shared task on Affect in Tweets, subtask 5: Detecting Emotions. The system combines lexicon, n-gram, style, syntactic and semantic features. For this multi-class multi-label problem, we created a classifier chain. This is an ensemble of eleven binary classifiers, one for each possible emotion category, where each model gets the predictions of the preceding models as additional features. The predicted labels are combined to get a multi-label representation of the predictions. Our system was ranked eleventh among thirty five participating teams, with a Jaccard accuracy of 52.0{\%} and macro- and micro-average F1-scores of 49.3{\%} and 64.0{\%}, respectively.
SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets
Authors: Flor Miriam Plaza-del-Arco, Salud Mar{\'\i}a Jim{\'e}nez-Zafra, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, Emotion Recognition, General Classification

Emotion classification is a new task that combines several disciplines including Artificial Intelligence and Psychology, although Natural Language Processing is perhaps the most challenging area. In this paper, we describe our participation in SemEval-2018 Task1: Affect in Tweets. In particular, we have participated in EI-oc, EI-reg and E-c subtasks for English and Spanish languages.