pwc-archive/papers-with-abstracts huggingface dataset:
A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text
Authors: Shashank Srivastava, Nebojsa Jojic
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Information Retrieval, Reading Comprehension, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Ordering, Text Generation

We present a generative probabilistic model of documents as sequences of sentences, and show that inference in it can lead to extraction of long-range latent discourse structure from a collection of documents. The approach is based on embedding sequences of sentences from longer texts into a 2- or 3-D spatial grids, in which one or two coordinates model smooth topic transitions, while the third captures the sequential nature of the modeled text. A significant advantage of our approach is that the learned models are naturally visualizable and interpretable, as semantic similarity and sequential structure are modeled along orthogonal directions in the grid. We show that the method is effective in capturing discourse structures in narrative text across multiple genres, including biographies, stories, and newswire reports. In particular, our method outperforms or is competitive with state-of-the-art generative approaches on tasks such as predicting the outcome of a story, and sentence ordering.
A Deep Relevance Model for Zero-Shot Document Filtering
Authors: Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentiment Analysis, Text Classification, Word Embeddings

In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand. As a fundamental task, information filtering therefore becomes a critical necessity. In this paper, we propose a novel deep relevance model for zero-shot document filtering, named DAZER. DAZER estimates the relevance between a document and a category by taking a small set of seed words relevant to the category. With pre-trained word embeddings from a large external corpus, DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process. The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner. Experiments on two document collections of two different tasks (i.e., topic categorization and sentiment analysis) demonstrate that DAZER significantly outperforms the existing alternative solutions, including the state-of-the-art deep relevance ranking models.
Disconnected Recurrent Neural Networks for Text Categorization
Authors: Baoxin Wang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Position, Sentiment Analysis, Text Classification

Recurrent neural network (RNN) has achieved remarkable performance in text categorization. RNN can model the entire sequence and capture long-term dependencies, but it does not do well in extracting key patterns. In contrast, convolutional neural network (CNN) is good at extracting local and position-invariant features. In this paper, we present a novel model named disconnected recurrent neural network (DRNN), which incorporates position-invariance into RNN. By limiting the distance of information flow in RNN, the hidden state at each time step is restricted to represent words near the current position. The proposed model makes great improvements over RNN and CNN models and achieves the best performance on several benchmark datasets for text categorization.
Neural Sparse Topical Coding
Authors: Min Peng, Qianqian Xie, Yanchun Zhang, Hua Wang, Xiuzhen Zhang, Jimin Huang, Gang Tian
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Topic Models, Word Embeddings

Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications. However, the extensions of these models require carefully tailored graphical models and re-deduced inference algorithms, limiting their variations and applications. We propose a novel sparsity-enhanced topic model, Neural Sparse Topical Coding (NSTC) base on a sparsity-enhanced topic model called Sparse Topical Coding (STC). It focuses on replacing the complex inference process with the back propagation, which makes the model easy to explore extensions. Moreover, the external semantic information of words in word embeddings is incorporated to improve the representation of short texts. To illustrate the flexibility offered by the neural network based framework, we present three extensions base on NSTC without re-deduced inference algorithms. Experiments on Web Snippet and 20Newsgroups datasets demonstrate that our models outperform existing methods.
Multi-Input Attention for Unsupervised OCR Correction
Authors: Rui Dong, David Smith
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Optical Character Recognition (OCR)

We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding. A sequence-to-sequence model with attention is applied for single-input correction, and a new decoder with multi-input attention averaging is developed to search for consensus among multiple sequences. We design two ways of training the correction model without human annotation, either training to match noisily observed textual variants or bootstrapping from a uniform error model. On two corpora of historical newspapers and books, we show that these unsupervised techniques cut the character and word error rates nearly in half on single inputs and, with the addition of multi-input decoding, can rival supervised methods.
Subword-level Word Vector Representations for Korean
Authors: Sungjoon Park, Jeongmin Byun, Sion Baek, Yongseok Cho, Alice Oh
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Document Classification, Language Modeling, Language Modelling, Machine Translation, Sentiment Analysis, Text Classification, Word Similarity

Research on distributed word representations is focused on widely-used languages such as English. Although the same methods can be used for other languages, language-specific knowledge can enhance the accuracy and richness of word vector representations. In this paper, we look at improving distributed word representations for Korean using knowledge about the unique linguistic structure of Korean. Specifically, we decompose Korean words into the jamo-level, beyond the character-level, allowing a systematic use of subword information. To evaluate the vectors, we develop Korean test sets for word similarity and analogy and make them publicly available. The results show that our simple method outperforms word2vec and character-level Skip-Grams on semantic and syntactic similarity and analogy tasks and contributes positively toward downstream NLP tasks such as sentiment analysis.
Cross-Domain Sentiment Classification with Target Domain Specific Information
Authors: Minlong Peng, Qi Zhang, Yu-Gang Jiang, Xuanjing Huang
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

The task of adopting a model with good performance to a target domain that is different from the source domain used for training has received considerable attention in sentiment analysis. Most existing approaches mainly focus on learning representations that are domain-invariant in both the source and target domains. Few of them pay attention to domain-specific information, which should also be informative. In this work, we propose a method to simultaneously extract domain specific and invariant representations and train a classifier on each of the representation, respectively. And we introduce a few target domain labeled data for learning domain-specific information. To effectively utilize the target domain labeled data, we train the domain invariant representation based classifier with both the source and target domain labeled data and train the domain-specific representation based classifier with only the target domain labeled data. These two classifiers then boost each other in a co-training style. Extensive sentiment analysis experiments demonstrated that the proposed method could achieve better performance than state-of-the-art methods.
A Helping Hand: Transfer Learning for Deep Sentiment Analysis
Authors: Xin Dong, Gerard de Melo
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Sentiment Analysis, Transfer Learning, Word Embeddings

Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memory-based component. We observe significant gains in effectiveness on a range of different datasets in seven different languages.
Modeling Deliberative Argumentation Strategies on Wikipedia
Authors: Khalid Al-Khatib, Henning Wachsmuth, Kevin Lang, Jakob Herpel, Matthias Hagen, Benno Stein
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

This paper studies how the argumentation strategies of participants in deliberative discussions can be supported computationally. Our ultimate goal is to predict the best next deliberative move of each participant. In this paper, we present a model for deliberative discussions and we illustrate its operationalization. Previous models have been built manually based on a small set of discussions, resulting in a level of abstraction that is not suitable for move recommendation. In contrast, we derive our model statistically from several types of metadata that can be used for move description. Applied to six million discussions from Wikipedia talk pages, our approach results in a model with 13 categories along three dimensions: discourse acts, argumentative relations, and frames. On this basis, we automatically generate a corpus with about 200,000 turns, labeled for the 13 categories. We then operationalize the model with three supervised classifiers and provide evidence that the proposed categories can be predicted.
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Authors: Piyush Sharma, Nan Ding, Sebastian Goodman, Radu Soricut
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Captioning

We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles. We achieve this by extracting and filtering image caption annotations from billions of webpages. We also present quantitative evaluations of a number of image captioning models and show that a model architecture based on Inception-ResNetv2 (Szegedy et al., 2016) for image-feature extraction and Transformer (Vaswani et al., 2017) for sequence modeling achieves the best performance when trained on the Conceptual Captions dataset.
Learning Translations via Images with a Massively Multilingual Image Dataset
Authors: John Hewitt, Daphne Ippolito, Brendan Callahan, Reno Kriz, Derry Tanti Wijaya, Chris Callison-Burch
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Retrieval, Machine Translation, Translation, Word Translation

We conduct the most comprehensive study to date into translating words via images. To facilitate research on the task, we introduce a large-scale multilingual corpus of images, each labeled with the word it represents. Past datasets have been limited to only a few high-resource languages and unrealistically easy translation settings. In contrast, we have collected by far the largest available dataset for this task, with images for approximately 10,000 words in each of 100 languages. We run experiments on a dozen high resource languages and 20 low resources languages, demonstrating the effect of word concreteness and part-of-speech on translation quality. {\%}We find that while image features work best for concrete nouns, they are sometimes effective on other parts of speech. To improve image-based translation, we introduce a novel method of predicting word concreteness from images, which improves on a previous state-of-the-art unsupervised technique. This allows us to predict when image-based translation may be effective, enabling consistent improvements to a state-of-the-art text-based word translation system. Our code and the Massively Multilingual Image Dataset (MMID) are available at \url{http://multilingual-images.org/}.
Pre- and In-Parsing Models for Neural Empty Category Detection
Authors: Yufei Chen, Yuan-Yuan Zhao, Weiwei Sun, Xiaojun Wan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Structured Prediction

Motivated by the positive impact of empty category on syntactic parsing, we study neural models for pre- and in-parsing detection of empty category, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.
Supervised Treebank Conversion: Data and Approaches
Authors: Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Multi-Task Learning, Sentence

Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance. However, previous work mainly focuses on unsupervised treebank conversion and has made little progress due to the lack of manually labeled data where each sentence has two syntactic trees complying with two different guidelines at the same time, referred as bi-tree aligned data. In this work, we for the first time propose the task of supervised treebank conversion. First, we manually construct a bi-tree aligned dataset containing over ten thousand sentences. Then, we propose two simple yet effective conversion approaches (pattern embedding and treeLSTM) based on the state-of-the-art deep biaffine parser. Experimental results show that 1) the two conversion approaches achieve comparable conversion accuracy, and 2) treebank conversion is superior to the widely used multi-task learning framework in multi-treebank exploitation and leads to significantly higher parsing accuracy.
Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers
Authors: Andre Cianflone, Yulan Feng, Jad Kabbara, Jackie Chi Kit Cheung
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Text Generation

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.
Word Error Rate Estimation for Speech Recognition: e-WER
Authors: Ahmed Ali, Steve Renals
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Word Embeddings

Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9{\%} WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER e-WER was 25.3{\%} for the three hours test set, while the actual WER was 28.5{\%}.
Active learning for deep semantic parsing
Authors: Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, Mark Johnson
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Active Learning, Semantic Parsing

Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and {``}overnight{''} data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks
Authors: Liat Ein Dor, Yosi Mass, Alon Halfon, Elad Venezian, Ilya Shnayderman, Ranit Aharonov, Noam Slonim
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Clustering, Collaborative Filtering, Document Summarization, Multi-Document Summarization, Semantic Similarity, Semantic Textual Similarity, Sentence, Text Clustering, Triplet

In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its section than to sentences from other sections. Based on this assumption, we use Wikipedia articles to automatically create a large dataset of weakly labeled sentence triplets, composed of a pivot sentence, one sentence from the same section and one from another section. We train a triplet network to embed sentences from the same section closer. To test the performance of the learned embeddings, we create and release a sentence clustering benchmark. We show that the triplet network learns useful thematic metrics, that significantly outperform state-of-the-art semantic similarity methods and multipurpose embeddings on the task of thematic clustering of sentences. We also show that the learned embeddings perform well on the task of sentence semantic similarity prediction.
Identification of Alias Links among Participants in Narratives
Authors: Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Girish Palshikar, Vasudeva Varma, Pushpak Bhattacharyya
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: coreference-resolution, Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering

Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications. This task becomes challenging because these participants are often referred to using multiple aliases. In this paper, we propose an approach based on linguistic knowledge for identification of aliases mentioned using proper nouns, pronouns or noun phrases with common noun headword. We use Markov Logic Network (MLN) to encode the linguistic knowledge for identification of aliases. We evaluate on four diverse history narratives of varying complexity. Our approach performs better than the state-of-the-art approach as well as a combination of standard named entity recognition and coreference resolution techniques.
Named Entity Recognition With Parallel Recurrent Neural Networks
Authors: Andrej {\v{Z}}ukov-Gregori{\v{c}}, Yoram Bachrach, Sam Coope
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Diversity, Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

We present a new architecture for named entity recognition. Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term. By distributing computation across multiple smaller LSTMs we find a significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
Authors: Prachi Jain, Pankaj Kumar, {Mausam}, Soumen Chakrabarti
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Entity Typing, Knowledge Base Completion, Knowledge Base Population, Knowledge Graph Completion, Link Prediction, Relation, Type prediction, Vocal Bursts Type Prediction

State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make entity predictions that are incompatible with the type required by the relation. In response, we enhance each base factorization with two type-compatibility terms between entity-relation pairs, and combine the signals in a novel manner. Without explicit supervision from a type catalog, our proposed modification obtains up to 7{\%} MRR gains over base models, and new state-of-the-art results on several datasets. Further analysis reveals that our models better represent the latent types of entities and their embeddings also predict supervised types better than the embeddings fitted by baseline models.
Ranking-Based Automatic Seed Selection and Noise Reduction for Weakly Supervised Relation Extraction
Authors: Van-Thuy Phi, Joan Santoso, Masashi Shimbo, Yuji Matsumoto
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Relation, Relation Extraction, Word Sense Disambiguation

This paper addresses the tasks of automatic seed selection for bootstrapping relation extraction, and noise reduction for distantly supervised relation extraction. We first point out that these tasks are related. Then, inspired by ranking relation instances and patterns computed by the HITS algorithm, and selecting cluster centroids using the K-means, LSA, or NMF method, we propose methods for selecting the initial seeds from an existing resource, or reducing the level of noise in the distantly labeled data. Experiments show that our proposed methods achieve a better performance than the baseline systems in both tasks.