pwc-archive/papers-with-abstracts huggingface dataset:
AirDialogue: An Environment for Goal-Oriented Dialogue Research
Authors: Wei Wei, Quoc Le, Andrew Dai, Jia Li
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue Generation, Reinforcement Learning, Text Generation

Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.
QuaSE: Sequence Editing under Quantifiable Guidance
Authors: Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Disentanglement, Sentence, Text Generation

We propose the task of Quantifiable Sequence Editing (QuaSE): editing an input sequence to generate an output sequence that satisfies a given numerical outcome value measuring a certain property of the sequence, with the requirement of keeping the main content of the input sequence. For example, an input sequence could be a word sequence, such as review sentence and advertisement text. For a review sentence, the outcome could be the review rating; for an advertisement, the outcome could be the click-through rate. The major challenge in performing QuaSE is how to perceive the outcome-related wordings, and only edit them to change the outcome. In this paper, the proposed framework contains two latent factors, namely, outcome factor and content factor, disentangled from the input sentence to allow convenient editing to change the outcome and keep the content. Our framework explores the pseudo-parallel sentences by modeling their content similarity and outcome differences to enable a better disentanglement of the latent factors, which allows generating an output to better satisfy the desired outcome and keep the content. The dual reconstruction structure further enhances the capability of generating expected output by exploiting the couplings of latent factors of pseudo-parallel sentences. For evaluation, we prepared a dataset of Yelp review sentences with the ratings as outcome. Extensive experimental results are reported and discussed to elaborate the peculiarities of our framework.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Authors: Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong pan, Chin-Yew Lin
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Data-to-Text Generation, Decoder, Quantization, Text Generation, Vocal Bursts Intensity Prediction

Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
Authors: Juntao Li, Yan Song, Haisong Zhang, Dongmin Chen, Shuming Shi, Dongyan Zhao, Rui Yan
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Text Generation

It is a challenging task to automatically compose poems with not only fluent expressions but also aesthetic wording. Although much attention has been paid to this task and promising progress is made, there exist notable gaps between automatically generated ones with those created by humans, especially on the aspects of term novelty and thematic consistency. Towards filling the gap, in this paper, we propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation, where the autoencoder part generates poems with novel terms and a discriminator is applied to adversarially learn their thematic consistency with their titles. Experimental results on a large poetry corpus confirm the validity and effectiveness of our model, where its automatic and human evaluation scores outperform existing models.
Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks
Authors: Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, Qifa Ke
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Question Generation, Question-Generation, Reading Comprehension, Sentence, Text Generation

Question generation, the task of automatically creating questions that can be answered by a certain span of text within a given passage, is important for question-answering and conversational systems in digital assistants such as Alexa, Cortana, Google Assistant and Siri. Recent sequence to sequence neural models have outperformed previous rule-based systems. Existing models mainly focused on using one or two sentences as the input. Long text has posed challenges for sequence to sequence neural models in question generation {--} worse performances were reported if using the whole paragraph (with multiple sentences) as the input. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. In this paper, we propose a maxout pointer mechanism with gated self-attention encoder to address the challenges of processing long text inputs for question generation. With sentence-level inputs, our model outperforms previous approaches with either sentence-level or paragraph-level inputs. Furthermore, our model can effectively utilize paragraphs as inputs, pushing the state-of-the-art result from 13.9 to 16.3 (BLEU{\_}4).
Answer-focused and Position-aware Neural Question Generation
Authors: Xingwu Sun, Jing Liu, Yajuan Lyu, wei he, Yanjun Ma, Shi Wang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Reading Comprehension, Position, Question Answering, Question Generation, Question-Generation, Reading Comprehension

In this paper, we focus on the problem of question generation (QG). Recent neural network-based approaches employ the sequence-to-sequence model which takes an answer and its context as input and generates a relevant question as output. However, we observe two major issues with these approaches: (1) The generated interrogative words (or question words) do not match the answer type. (2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer. To address these two issues, we propose an answer-focused and position-aware neural question generation model. (1) By answer-focused, we mean that we explicitly model question word generation by incorporating the answer embedding, which can help generate an interrogative word matching the answer type. (2) By position-aware, we mean that we model the relative distance between the context words and the answer. Hence the model can be aware of the position of the context words when copying them to generate a question. We conduct extensive experiments to examine the effectiveness of our model. The experimental results show that our model significantly improves the baseline and outperforms the state-of-the-art system.
Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation
Authors: Jingjing Xu, Xuancheng Ren, Junyang Lin, Xu sun
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue Generation, Diversity, Generative Adversarial Network, Language Modeling, Language Modelling, Machine Translation, Review Generation, Text Generation, Text Summarization

Existing text generation methods tend to produce repeated and {''}boring{''} expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeatedly generated text and high reward for {''}novel{''} and fluent text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our model can generate substantially more diverse and informative text than existing baselines.
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement
Authors: Cheng Yang, Maosong Sun, Xiaoyuan Yi, Wenhao Li
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Disentanglement, Machine Translation, Style Transfer

The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing. Most previous works on automatic Chinese poetry generation focused on improving the coherency among lines. Some work explored style transfer but suffered from expensive expert labeling of poem styles. In this paper, we target on stylistic poetry generation in a fully unsupervised manner for the first time. We propose a novel model which requires no supervised style labeling by incorporating mutual information, a concept in information theory, into modeling. Experimental results show that our model is able to generate stylistic poems without losing fluency and coherency.
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity
Authors: Xinnuo Xu, Ond{\v{r}}ej Du{\v{s}}ek, Ioannis Konstas, Verena Rieser
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Dialogue Generation, Diversity

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.
Incorporating Background Knowledge into Video Description Generation
Authors: Spencer Whitehead, Heng Ji, Mohit Bansal, Shih-Fu Chang, Clare Voss
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Text Generation, Video Captioning, Video Description

Most previous efforts toward video captioning focus on generating generic descriptions, such as, {``}A man is talking.{''} We collect a news video dataset to generate enriched descriptions that include important background knowledge, such as named entities and related events, which allows the user to fully understand the video content. We develop an approach that uses video meta-data to retrieve topically related news documents for a video and extracts the events and named entities from these documents. Then, given the video as well as the extracted events and entities, we generate a description using a Knowledge-aware Video Description network. The model learns to incorporate entities found in the topically related documents into the description via an entity pointer network and the generation procedure is guided by the event and entity types from the topically related documents through a knowledge gate, which is a gating mechanism added to the model{'}s decoder that takes a one-hot vector of these types. We evaluate our approach on the new dataset of news videos we have collected, establishing the first benchmark for this dataset as well as proposing a new metric to evaluate these descriptions.
Multimodal Differential Network for Visual Question Generation
Authors: Badri Narayana Patro, S. Kumar, eep, Vinod Kumar Kurmi, Vinay Namboodiri
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Image Captioning, Natural Questions, Question Answering, Question Generation, Question-Generation, Visual Question Answering (VQA)

Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).
Abstractive Text-Image Summarization Using Multi-Modal Attentional Hierarchical RNN
Authors: Jingqiang Chen, Hai Zhuge
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Extractive Text Summarization, Text Summarization

Rapid growth of multi-modal documents on the Internet makes multi-modal summarization research necessary. Most previous research summarizes texts or images separately. Recent neural summarization research shows the strength of the Encoder-Decoder model in text summarization. This paper proposes an abstractive text-image summarization model using the attentional hierarchical Encoder-Decoder model to summarize a text document and its accompanying images simultaneously, and then to align the sentences and images in summaries. A multi-modal attentional mechanism is proposed to attend original sentences, images, and captions when decoding. The DailyMail dataset is extended by collecting images and captions from the Web. Experiments show our model outperforms the neural abstractive and extractive text summarization methods that do not consider images. In addition, our model can generate informative summaries of images.
Improving Neural Abstractive Document Summarization with Structural Regularization
Authors: Wei Li, Xinyan Xiao, Yajuan Lyu, Yuanzhuo Wang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Document Summarization, Machine Translation, Sentence, Sentence Summarization, Text Generation, Text Summarization

Recent neural sequence-to-sequence models have shown significant progress on short text summarization. However, for document summarization, they fail to capture the long-term structure of both documents and multi-sentence summaries, resulting in information loss and repetitions. In this paper, we propose to leverage the structural information of both documents and multi-sentence summaries to improve the document summarization performance. Specifically, we import both structural-compression and structural-coverage regularization into the summarization process in order to capture the information compression and information coverage properties, which are the two most important structural properties of document summarization. Experimental results demonstrate that the structural regularization improves the document summarization performance significantly, which enables our model to generate more informative and concise summaries, and thus significantly outperforms state-of-the-art neural abstractive methods.
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
Authors: Yizhu Liu, Zhiyi Luo, Kenny Zhu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Machine Translation, Semantic Similarity, Semantic Textual Similarity

Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths. Because generated summaries are used in difference scenarios which may have space or length constraints, the ability to control the summary length in abstractive summarization is an important problem. In this paper, we propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. The results show that this approach generates high-quality summaries with user defined length, and outperforms the baselines consistently in terms of ROUGE score, length variations and semantic similarity.
MSMO: Multimodal Summarization with Multimodal Output
Authors: Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Cheng-qing Zong
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Informativeness, Text Summarization

Multimodal summarization has drawn much attention due to the rapid growth of multimedia data. The output of the current multimodal summarization systems is usually represented in texts. However, we have found through experiments that multimodal output can significantly improve user satisfaction for informativeness of summaries. In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO). To handle this task, we first collect a large-scale dataset for MSMO research. We then propose a multimodal attention model to jointly generate text and select the most relevant image from the multimodal input. Finally, to evaluate multimodal outputs, we construct a novel multimodal automatic evaluation (MMAE) method which considers both intra-modality salience and inter-modality relevance. The experimental results show the effectiveness of MMAE.
Frustratingly Easy Model Ensemble for Abstractive Summarization
Authors: Hayato Kobayashi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization, Density Estimation, Headline Generation, model, Model Compression, Question Answering, Text Generation

Ensemble methods, which combine multiple models at decoding time, are now widely known to be effective for text-generation tasks. However, they generally increase computational costs, and thus, there have been many studies on compressing or distilling ensemble models. In this paper, we propose an alternative, simple but effective unsupervised ensemble method, \textit{post-ensemble}, that combines multiple models by selecting a majority-like output in post-processing. We theoretically prove that our method is closely related to kernel density estimation based on the von Mises-Fisher kernel. Experimental results on a news-headline-generation task show that the proposed method performs better than the current ensemble methods.
Automatic Pyramid Evaluation Exploiting EDU-based Extractive Reference Summaries
Authors: Tsutomu Hirao, Hidetaka Kamigaito, Masaaki Nagata
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Textual Similarity

This paper tackles automation of the pyramid method, a reliable manual evaluation framework. To construct a pyramid, we transform human-made reference summaries into extractive reference summaries that consist of Elementary Discourse Units (EDUs) obtained from source documents and then weight every EDU by counting the number of extractive reference summaries that contain the EDU. A summary is scored by the correspondences between EDUs in the summary and those in the pyramid. Experiments on DUC and TAC data sets show that our methods strongly correlate with various manual evaluations.
A Nil-Aware Answer Extraction Framework for Question Answering
Authors: Souvik Kundu, Hwee Tou Ng
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Reading Comprehension, valid

Recently, there has been a surge of interest in reading comprehension-based (RC) question answering (QA). However, current approaches suffer from an impractical assumption that every question has a valid answer in the associated passage. A practical QA system must possess the ability to determine whether a valid answer exists in a given text passage. In this paper, we focus on developing QA systems that can extract an answer for a question if and only if the associated passage contains an answer. If the associated passage does not contain any valid answer, the QA system will correctly return Nil. We propose a novel nil-aware answer span extraction framework that is capable of returning Nil or a text span from the associated passage as an answer in a single step. We show that our proposed framework can be easily integrated with several recently proposed QA models developed for reading comprehension and can be trained in an end-to-end fashion. Our proposed nil-aware answer extraction neural network decomposes pieces of evidence into relevant and irrelevant parts and then combines them to infer the existence of any answer. Experiments on the NewsQA dataset show that the integration of our proposed framework significantly outperforms several strong baseline systems that use pipeline or threshold-based approaches.
A Neural Local Coherence Model for Text Quality Assessment
Authors: Mohsen Mesgar, Michael Strube
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Word Embeddings

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.