pwc-archive/papers-with-abstracts huggingface dataset:
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information
Authors: Jiaxin Pei, Chenliang Li
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Response Generation, Sentence, Translation

How to generate relevant and informative responses is one of the core topics in response generation area. Following the task formulation of machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. To realize this mapping, existing works tend to design intuitive but complex models. However, the relevant information existed in large dialogue corpus is mainly overlooked. In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation. Specifically, we devise two simple approaches in S2SPMN to select the relevant information (named prototypes) from the dialogue corpus. These prototypes are then saved into prototype memory network (PMN). Furthermore, a hierarchical attention mechanism is devised to extract the semantic information from the PMN to assist the response generation process. Empirical studies reveal the advantage of our model over several classical and strong baselines.
Training for Diversity in Image Paragraph Captioning
Authors: Luke Melas-Kyriazi, Alex Rush, er, George Han
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Diversity, Image Captioning, Image Paragraph Captioning, Machine Translation, Object Detection, Policy Gradient Methods, Text Generation

Image paragraph captioning models aim to produce detailed descriptions of a source image. These models use similar techniques as standard image captioning models, but they have encountered issues in text generation, notably a lack of diversity between sentences, that have limited their effectiveness. In this work, we consider applying sequence-level training for this task. We find that standard self-critical training produces poor results, but when combined with an integrated penalty on trigram repetition produces much more diverse paragraphs. This simple training approach improves on the best result on the Visual Genome paragraph captioning dataset from 16.9 to 30.6 CIDEr, with gains on METEOR and BLEU as well, without requiring any architectural changes.
A Graph-theoretic Summary Evaluation for ROUGE
Authors: Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Semantic Similarity, Semantic Textual Similarity, Text Summarization

ROUGE is one of the first and most widely used evaluation metrics for text summarization. However, its assessment merely relies on surface similarities between peer and model summaries. Consequently, ROUGE is unable to fairly evaluate summaries including lexical variations and paraphrasing. We propose a graph-based approach adopted into ROUGE to evaluate summaries based on both lexical and semantic similarities. Experiment results over TAC AESOP datasets show that exploiting the lexico-semantic similarity of the words used in summaries would significantly help ROUGE correlate better with human judgments.
Evaluating Multiple System Summary Lengths: A Case Study
Authors: Ori Shapira, David Gabay, Hadar Ronen, Judit Bar-Ilan, Yael Amsterdamer, Ani Nenkova, Ido Dagan
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.
On the Abstractiveness of Neural Document Summarization
Authors: Fangfang Zhang, Jin-Ge Yao, Rui Yan
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Decoder, Document Summarization

Many modern neural document summarization systems based on encoder-decoder networks are designed to produce abstractive summaries. We attempted to verify the degree of abstractiveness of modern neural abstractive summarization systems by calculating overlaps in terms of various types of units. Upon the observation that many abstractive systems tend to be near-extractive in practice, we also implemented a pure copy system, which achieved comparable results as abstractive summarizers while being far more computationally efficient. These findings suggest the possibility for future efforts towards more efficient systems that could better utilize the vocabulary in the original document.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning
Authors: Yucheng Wang, Zhongyu Wei, Yaqian Zhou, Xuanjing Huang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Relation Classification, Text Generation

Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
Authors: Kazuya Shimura, Jiyi Li, Fumiyo Fukumoto
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Extreme Multi-Label Classification, Multi-Label Classification, Text Categorization

We focus on the multi-label categorization task for short texts and explore the use of a hierarchical structure (HS) of categories. In contrast to the existing work using non-hierarchical flat model, the method leverages the hierarchical relations between the pre-defined categories to tackle the data sparsity problem. The lower the HS level, the less the categorization performance. Because the number of training data per category in a lower level is much smaller than that in an upper level. We propose an approach which can effectively utilize the data in the upper levels to contribute the categorization in the lower levels by applying the Convolutional Neural Network (CNN) with a fine-tuning technique. The results using two benchmark datasets show that proposed method, Hierarchical Fine-Tuning based CNN (HFT-CNN) is competitive with the state-of-the-art CNN based methods.
A Hierarchical Neural Attention-based Text Classifier
Authors: Koustuv Sinha, Yue Dong, Jackie Chi Kit Cheung, Derek Ruths
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, text-classification, Text Classification

Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability.
Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
Authors: Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Document Classification, General Classification, Text Classification, Topic Models

We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.
Topic Intrusion for Automatic Topic Model Evaluation
Authors: Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Information Retrieval, model, Topic Models

Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task {---} the task of guessing an outlier topic given a document and a few topics {---} and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.
Supervised and Unsupervised Methods for Robust Separation of Section Titles and Prose Text in Web Documents
Authors: Abhijith Athreya Mysore Gopinath, Shomir Wilson, Norman Sadeh
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Information Retrieval, Question Answering

The text in many web documents is organized into a hierarchy of section titles and corresponding prose content, a structure which provides potentially exploitable information on discourse structure and topicality. However, this organization is generally discarded during text collection, and collecting it is not straightforward: the same visual organization can be implemented in a myriad of different ways in the underlying HTML. To remedy this, we present a flexible system for automatically extracting the hierarchical section titles and prose organization of web documents irrespective of differences in HTML representation. This system uses features from syntax, semantics, discourse and markup to build two models which classify HTML text into section titles and prose text. When tested on three different domains of web text, our domain-independent system achieves an overall precision of 0.82 and a recall of 0.98. The domain-dependent variation produces very high precision (0.99) at the expense of recall (0.75). These results exhibit a robust level of accuracy suitable for enhancing question answering, information extraction, and summarization.
Self-Governing Neural Networks for On-Device Short Text Classification
Authors: Sujith Ravi, Zornitsa Kozareva
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, Dialog Act Classification, Dialogue Act Classification, General Classification, text-classification, Text Classification, Word Embeddings

Deep neural networks reach state-of-the-art performance for wide range of natural language processing, computer vision and speech applications. Yet, one of the biggest challenges is running these complex networks on devices such as mobile phones or smart watches with tiny memory footprint and low computational capacity. We propose on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. We conduct extensive evaluation on dialog act classification and show significant improvement over state-of-the-art results. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy.
Generating Syntactic Paraphrases
Authors: Emilie Colin, Claire Gardent
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Data-to-Text Generation, Machine Translation, Paraphrase Generation, Question Answering, Semantic Parsing, Sentence Compression, Text Generation

We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, text-to-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input.
Conversational Decision-Making Model for Predicting the King's Decision in the Annals of the Joseon Dynasty
Authors: JinYeong Bak, Alice Oh
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decision Making

Styles of leaders when they make decisions in groups vary, and the different styles affect the performance of the group. To understand the key words and speakers associated with decisions, we initially formalize the problem as one of predicting leaders{'} decisions from discussion with group members. As a dataset, we introduce conversational meeting records from a historical corpus, and develop a hierarchical RNN structure with attention and pre-trained speaker embedding in the form of a, Conversational Decision Making Model (CDMM). The CDMM outperforms other baselines to predict leaders{'} final decisions from the data. We explain why CDMM works better than other methods by showing the key words and speakers discovered from the attentions as evidence.
A Dataset for Telling the Stories of Social Media Videos
Authors: Sp Gella, ana, Mike Lewis, Marcus Rohrbach
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Video Captioning, Video Description

Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.
Put It Back: Entity Typing with Language Model Enhancement
Authors: Ji Xin, Hao Zhu, Xu Han, Zhiyuan Liu, Maosong Sun
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Linking, Entity Typing, Language Modeling, Language Modelling, Question Answering, Relation Extraction

Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from \url{https://github.com/thunlp/LME}.
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
Authors: Yiqing Zhang, Jianzhong Qi, Rui Zhang, Chu Yin, ong
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Diversity

Publication information in a researcher{'}s academic homepage provides insights about the researcher{'}s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8{\%} in F1-score.
Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction
Authors: Ge Shi, Chong Feng, Lifu Huang, Boliang Zhang, Heng Ji, Lejian Liao, He-Yan Huang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering, Relation, Relation Extraction, Word Embeddings

Relation Extraction suffers from dramatical performance decrease when training a model on one genre and directly applying it to a new genre, due to the distinct feature distributions. Previous studies address this problem by discovering a shared space across genres using manually crafted features, which requires great human effort. To effectively automate this process, we design a genre-separation network, which applies two encoders, one genre-independent and one genre-shared, to explicitly extract genre-specific and genre-agnostic features. Then we train a relation classifier using the genre-agnostic features on the source genre and directly apply to the target genre. Experiment results on three distinct genres of the ACE dataset show that our approach achieves up to 6.1{\%} absolute F1-score gain compared to previous methods. By incorporating a set of external linguistic features, our approach outperforms the state-of-the-art by 1.7{\%} absolute F1 gain. We make all programs of our model publicly available for research purpose
Effective Use of Context in Noisy Entity Linking
Authors: David Mueller, Greg Durrett
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Linking

To disambiguate between closely related concepts, entity linking systems need to effectively distill cues from their context, which may be quite noisy. We investigate several techniques for using these cues in the context of noisy entity linking on short texts. Our starting point is a state-of-the-art attention-based model from prior work; while this model{'}s attention typically identifies context that is topically relevant, it fails to identify some of the most indicative surface strings, especially those exhibiting lexical overlap with the true title. Augmenting the model with convolutional networks over characters still leaves it largely unable to pick up on these cues compared to sparse features that target them directly, indicating that automatically learning how to identify relevant character-level context features is a hard problem. Our final system outperforms past work on the WikilinksNED test set by 2.8{\%} absolute.
Facts That Matter
Authors: Marco Ponza, Luciano del Corro, Gerhard Weikum
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Clustering, Diversity, Entity Linking, Open Information Extraction, Question Answering, Relation Extraction, Text Summarization

This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. We also present SalIE, the first fact salience system. SalIE is unsupervised and knowledge agnostic, based on open information extraction to detect facts in natural language text, PageRank to determine their relevance, and clustering to promote diversity. We compare SalIE with several baselines (including positional, standard for saliency tasks), and in an extrinsic evaluation, with state-of-the-art automatic text summarizers. SalIE outperforms baselines and text summarizers showing that facts are an effective way to compress information.
Translating a Math Word Problem to a Expression Tree
Authors: Lei Wang, Yan Wang, Deng Cai, Dongxiang Zhang, Xiaojiang Liu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Math, Math Word Problem Solving, Semantic Parsing

Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.
Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection
Authors: Massimo Nicosia, Aless Moschitti, ro
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering, Question Answering, Sentence

State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms.
A dataset and baselines for sequential open-domain question answering
Authors: Ahmed Elgohary, Chen Zhao, Jordan Boyd-Graber
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Information Retrieval, Open-Domain Question Answering, Question Answering, Reading Comprehension

Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at \url{http://sequential.qanta.org}.