pwc-archive/papers-with-abstracts huggingface dataset:
Contextual String Embeddings for Sequence Labeling
Authors: Alan Akbik, Duncan Blythe, Rol Vollgraf
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Chunking, Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Word Embeddings

Recent advances in language modeling using recurrent neural networks have made it viable to model language as distributions over characters. By learning to predict the next character on the basis of previous characters, such models have been shown to automatically internalize linguistic concepts such as words, sentences, subclauses and even sentiment. In this paper, we propose to leverage the internal states of a trained character language model to produce a novel type of word embedding which we refer to as contextual string embeddings. Our proposed embeddings have the distinct properties that they (a) are trained without any explicit notion of words and thus fundamentally model words as sequences of characters, and (b) are contextualized by their surrounding text, meaning that the same word will have different embeddings depending on its contextual use. We conduct a comparative evaluation against previous embeddings and find that our embeddings are highly useful for downstream tasks: across four classic sequence labeling tasks we consistently outperform the previous state-of-the-art. In particular, we significantly outperform previous work on English and German named entity recognition (NER), allowing us to report new state-of-the-art F1-scores on the CoNLL03 shared task. We release all code and pre-trained language models in a simple-to-use framework to the research community, to enable reproduction of these experiments and application of our proposed embeddings to other tasks: https://github.com/zalandoresearch/flair
Learning Word Meta-Embeddings by Autoencoding
Authors: Danushka Bollegala, Cong Bao
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dependency Parsing, Machine Translation, Part-Of-Speech Tagging, Sentiment Analysis, Word Embeddings

Distributed word embeddings have shown superior performances in numerous Natural Language Processing (NLP) tasks. However, their performances vary significantly across different tasks, implying that the word embeddings learnt by those methods capture complementary aspects of lexical semantics. Therefore, we believe that it is important to combine the existing word embeddings to produce more accurate and complete meta-embeddings of words. We model the meta-embedding learning problem as an autoencoding problem, where we would like to learn a meta-embedding space that can accurately reconstruct all source embeddings simultaneously. Thereby, the meta-embedding space is enforced to capture complementary information in different source embeddings via a coherent common embedding space. We propose three flavours of autoencoded meta-embeddings motivated by different requirements that must be satisfied by a meta-embedding. Our experimental results on a series of benchmark evaluations show that the proposed autoencoded meta-embeddings outperform the existing state-of-the-art meta-embeddings in multiple tasks.
GenSense: A Generalized Sense Retrofitting Model
Authors: Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Yow-Ting Shiue, Hsin-Hsi Chen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: model, Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation, Word Similarity

With the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To address this issue, some researches propose sense embedding learning algorithms. In this paper, we present a generalized model from existing sense retrofitting model. The generalization takes three major components: semantic relations between the senses, the relation strength and the semantic strength. In the experiment, we show that the generalized model can outperform previous approaches in three types of experiment: semantic relatedness, contextual word similarity and semantic difference.
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System
Authors: Yu Wang, Abhishek Patel, Hongxia Jin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Deep Reinforcement Learning, Named Entity Recognition (NER), reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Slot Filling, Spoken Language Understanding

In this paper, a new deep reinforcement learning based augmented general tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based sequence labeling model and a deep reinforcement learning (DRL) based augmented tagger. The augmented tagger helps improve system performance by modeling the data with minority tags. The new system is evaluated on SLU and NLU sequence labeling tasks using ATIS and CoNLL-2003 benchmark datasets, to demonstrate the new system{'}s outstanding performance on general tagging tasks. Evaluated by F1 scores, it shows that the new system outperforms the current state-of-the-art model on ATIS dataset by 1.9{\%} and that on CoNLL-2003 dataset by 1.4{\%}.
Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types
Authors: Paul Felt, Eric Ringger, Jordan Boyd-Graber, Kevin Seppi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Annotated corpora enable supervised machine learning and data analysis. To reduce the cost of manual annotation, tasks are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. We approach the problem of crowdsourcing using a framework for learning from rich prior knowledge, and we identify a family of crowdsourcing models with the novel ability to combine annotations with differing structures: e.g., document labels and word labels. Annotator judgments are given in the form of the predicted expected value of measurement functions computed over annotations and the data, unifying annotation models. Our model, a specific instance of this framework, compares favorably with previous work. Furthermore, it enables active sample selection, jointly selecting annotator, data item, and annotation structure to reduce annotation effort.
Reproducing and Regularizing the SCRN Model
Authors: Olzhas Kabdolov, Zhenisbek Assylbekov, Rustem Takhanov
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling, model

We reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naive dropout, variational dropout, and weight tying. We show that when regularized and optimized appropriately the SCRN model can achieve performance comparable with the ubiquitous LSTM model in language modeling task on English data, while outperforming it on non-English data.
Measuring the Diversity of Automatic Image Descriptions
Authors: Emiel van Miltenburg, Desmond Elliott, Piek Vossen
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Diversity, Image Description, Text Generation

Automatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of generic descriptions as a lack of diversity in the output, which we quantify using established metrics and two new metrics that frame image description as a word recall task. This framing allows us to evaluate system performance on the head of the vocabulary, as well as on the long tail, where system performance degrades. We use these metrics to examine the diversity of the sentences generated by nine state-of-the-art systems on the MS COCO data set. We find that the systems trained with maximum likelihood objectives produce less diverse output than those trained with additional adversarial objectives. However, the adversarially-trained models only produce more types from the head of the vocabulary and not the tail. Besides vocabulary-based methods, we also look at the compositional capacity of the systems, specifically their ability to create compound nouns and prepositional phrases of different lengths. We conclude that there is still much room for improvement, and offer a toolkit to measure progress towards the goal of generating more diverse image descriptions.
Extractive Headline Generation Based on Learning to Rank for Community Question Answering
Authors: Tatsuru Higurashi, Hayato Kobayashi, Takeshi Masuyama, Kazuma Murao
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Community Question Answering, Headline Generation, Learning-To-Rank, Question Answering

User-generated content such as the questions on community question answering (CQA) forums does not always come with appropriate headlines, in contrast to the news articles used in various headline generation tasks. In such cases, we cannot use paired supervised data, e.g., pairs of articles and headlines, to learn a headline generation model. To overcome this problem, we propose an extractive headline generation method based on learning to rank for CQA that extracts the most informative substring from each question as its headline. Experimental results show that our method outperforms several baselines, including a prefix-based method, which is widely used in real services.
A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task
Authors: Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, Zhenglu Yang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Common Sense Reasoning, Feature Engineering, Text Generation

Enabling a mechanism to understand a temporal story and predict its ending is an interesting issue that has attracted considerable attention, as in case of the ROC Story Cloze Task (SCT). In this paper, we develop a multi-attention-based neural network (MANN) with well-designed optimizations, like Highway Network, and concatenated features with embedding representations into the hierarchical neural network model. Considering the particulars of the specific task, we thoughtfully extend MANN with external knowledge resources, exceeding state-of-the-art results obviously. Furthermore, we develop a thorough understanding of our model through a careful hand analysis on a subset of the stories. We identify what traits of MANN contribute to its outperformance and how external knowledge is obtained in such an ending prediction task.
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators
Authors: Zhihao Fan, Zhongyu Wei, Siyuan Wang, Yang Liu, Xuanjing Huang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Natural Questions, Question Answering, Question Generation, Question-Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scene Understanding, Text Generation, Visual Question Answering (VQA)

Visual Question Generation (VQG) aims to ask natural questions about an image automatically. Existing research focus on training model to fit the annotated data set that makes it indifferent from other language generation tasks. We argue that natural questions need to have two specific attributes from the perspectives of content and linguistic respectively, namely, natural and human-written. Inspired by the setting of discriminator in adversarial learning, we propose two discriminators, one for each attribute, to enhance the training. We then use the reinforcement learning framework to incorporate scores from the two discriminators as the reward to guide the training of the question generator. Experimental results on a benchmark VQG dataset show the effectiveness and robustness of our model compared to some state-of-the-art models in terms of both automatic and human evaluation metrics.
Treat us like the sequences we are: Prepositional Paraphrasing of Noun Compounds using LSTM
Authors: Girishkumar Ponkiya, Kevin Patel, Pushpak Bhattacharyya, Girish Palshikar
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Question Answering

Interpreting noun compounds is a challenging task. It involves uncovering the underlying predicate which is dropped in the formation of the compound. In most cases, this predicate is of the form VERB+PREP. It has been observed that uncovering the preposition is a significant step towards uncovering the predicate. In this paper, we attempt to paraphrase noun compounds using prepositions. We consider noun compounds and their corresponding prepositional paraphrases as parallelly aligned sequences of words. This enables us to adapt different architectures from cross-lingual embedding literature. We choose the architecture where we create representations of both noun compound (source sequence) and its corresponding prepositional paraphrase (target sequence), such that their sim- ilarity is high. We use LSTMs to learn these representations. We use these representations to decide the correct preposition. Our experiments show that this approach performs considerably well on different datasets of noun compounds that are manually annotated with prepositions.
Recognizing Humour using Word Associations and Humour Anchor Extraction
Authors: Andrew Cattle, Xiaojuan Ma
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Semantic Textual Similarity, Word Embeddings

This paper attempts to marry the interpretability of statistical machine learning approaches with the more robust models of joke structure and joke semantics capable of being learned by neural models. Specifically, we explore the use of semantic relatedness features based on word associations, rather than the more common Word2Vec similarity, on a binary humour identification task and identify several factors that make word associations a better fit for humour. We also explore the effects of using joke structure, in the form of humour anchors (Yang et al., 2015), for improving the performance of semantic features and show that, while an intriguing idea, humour anchors contain several pitfalls that can hurt performance.
A Retrospective Analysis of the Fake News Challenge Stance-Detection Task
Authors: Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, Iryna Gurevych
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Stance Classification, Stance Detection

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1{'}s experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1{'}s proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods{'} ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research.
Exploiting Syntactic Structures for Humor Recognition
Authors: Lizhen Liu, Donghai Zhang, Wei Song
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Humor recognition is an interesting and challenging task in natural language processing. This paper proposes to exploit syntactic structure features to enhance humor recognition. Our method achieves significant improvements compared with humor theory driven baselines. We found that some syntactic structure features consistently correlate with humor, which indicate interesting linguistic phenomena. Both the experimental results and the analysis demonstrate that humor can be viewed as a kind of style and content independent syntactic structures can help identify humor and have good interpretability.
An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection
Authors: Zhenni You, Tieyun Qian, Bing Liu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Attribute, Spam detection

Spam detection has long been a research topic in both academic and industry due to its wide applications. Previous studies are mainly focused on extracting linguistic or behavior features to distinguish the spam and legitimate reviews. Such features are either ineffective or take long time to collect and thus are hard to be applied to cold-start spam review detection tasks. Recent advance leveraged the neural network to encode the textual and behavior features for the cold-start problem. However, the abundant attribute information are largely neglected by the existing framework. In this paper, we propose a novel deep learning architecture for incorporating entities and their inherent attributes from various domains into a unified framework. Specifically, our model not only encodes the entities of reviewer, item, and review, but also their attributes such as location, date, price ranges. Furthermore, we present a domain classifier to adapt the knowledge from one domain to the other. With the abundant attributes in existing entities and knowledge in other domains, we successfully solve the problem of data scarcity in the cold-start settings. Experimental results on two Yelp datasets prove that our proposed framework significantly outperforms the state-of-the-art methods.
A Pseudo Label based Dataless Naive Bayes Algorithm for Text Classification with Seed Words
Authors: Xi-Ming Li, Bo Yang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Pseudo Label, text-classification, Text Classification

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset.
Visual Question Answering Dataset for Bilingual Image Understanding: A Study of Cross-Lingual Transfer Using Attention Maps
Authors: Nobuyuki Shimizu, Na Rong, Takashi Miyazaki
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Cross-Lingual Transfer, Image Captioning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) is a challenging task that requires a computer system to understand both a question and an image. While there is much research on VQA in English, there is a lack of datasets for other languages, and English annotation is not directly applicable in those languages. To deal with this, we have created a Japanese VQA dataset by using crowdsourced annotation with images from the Visual Genome dataset. This is the first such dataset in Japanese. As another contribution, we propose a cross-lingual method for making use of English annotation to improve a Japanese VQA system. The proposed method is based on a popular VQA method that uses an attention mechanism. We use attention maps generated from English questions to help improve the Japanese VQA task. The proposed method experimentally performed better than simply using a monolingual corpus, which demonstrates the effectiveness of using attention maps to transfer cross-lingual information.
Style Detection for Free Verse Poetry from Text and Speech
Authors: Timo Baumann, Hussein Hussein, Burkhard Meyer-Sickendiek
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making, Style Detection

Modern and post-modern free verse poems feature a large and complex variety in their poetic prosodies that falls along a continuum from a more fluent to a more disfluent and choppy style. As the poets of modernism overcame rhyme and meter, they oriented themselves in these two opposing directions, creating a free verse spectrum that calls for new analyses of prosodic forms. We present a method, grounded in philological analysis and current research on cognitive (dis)fluency, for automatically analyzing this spectrum. We define and relate six classes of poetic styles (ranging from parlando to lettristic decomposition) by their gradual differentiation. Based on this discussion, we present a model for automatic prosodic classification of spoken free verse poetry that uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We evaluate our model on a large corpus of German author-read post-modern poetry and find that classes can reliably be differentiated, reaching a weighted f-measure of 0.73, when combining textual and phonetic evidence. In our further analyses, we validate the model{'}s decision-making process, the philologically hypothesized continuum of fluency and investigate the relative importance of various features.
A Neural Question Answering Model Based on Semi-Structured Tables
Authors: Hao Wang, Xiaodong Zhang, Shuming Ma, Xu sun, Houfeng Wang, Mengxiang Wang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Knowledge Graphs, Multiple-choice, Question Answering

Most question answering (QA) systems are based on raw text and structured knowledge graph. However, raw text corpora are hard for QA system to understand, and structured knowledge graph needs intensive manual work, while it is relatively easy to obtain semi-structured tables from many sources directly, or build them automatically. In this paper, we build an end-to-end system to answer multiple choice questions with semi-structured tables as its knowledge. Our system answers queries by two steps. First, it finds the most similar tables. Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer. The system is evaluated with TabMCQ dataset, and gets a huge improvement compared to the state of the art.