pwc-archive/papers-with-abstracts huggingface dataset:
Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering
Authors: Shangwen Lv, Daya Guo, Jingjing Xu, Duyu Tang, Nan Duan, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Songlin Hu
Date: 2019-09-09 00:00:00
Tasks: Common Sense Reasoning, Graph Attention, Question Answering, Representation Learning

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent works either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA leaderboard.
Let's Ask Again: Refine Network for Automatic Question Generation
Authors: Preksha Nema, Akash Kumar Mohankumar, Mitesh M. Khapra, Balaji Vasan Srinivasan, Balaraman Ravindran
Date: 2019-08-31 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Question Generation, Question-Generation

In this work, we focus on the task of Automatic Question Generation (AQG) where given a passage and an answer the task is to generate the corresponding question. It is desired that the generated question should be (i) grammatically correct (ii) answerable from the passage and (iii) specific to the given answer. An analysis of existing AQG models shows that they produce questions which do not adhere to one or more of {the above-mentioned qualities}. In particular, the generated questions look like an incomplete draft of the desired question with a clear scope for refinement. {To alleviate this shortcoming}, we propose a method which tries to mimic the human process of generating questions by first creating an initial draft and then refining it. More specifically, we propose Refine Network (RefNet) which contains two decoders. The second decoder uses a dual attention network which pays attention to both (i) the original passage and (ii) the question (initial draft) generated by the first decoder. In effect, it refines the question generated by the first decoder, thereby making it more correct and complete. We evaluate RefNet on three datasets, \textit{viz.}, SQuAD, HOTPOT-QA, and DROP, and show that it outperforms existing state-of-the-art methods by 7-16\% on all of these datasets. Lastly, we show that we can improve the quality of the second decoder on specific metrics, such as, fluency and answerability by explicitly rewarding revisions that improve on the corresponding metric during training. The code has been made publicly available \footnote{https://github.com/PrekshaNema25/RefNet-QG}
Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset
Authors: Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, Kyu-Young Kim
Date: 2019-09-01 00:00:00
Proceeding: IJCNLP 2019 11

A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data. To help satisfy this elementary requirement, we introduce the initial release of the Taskmaster-1 dataset which includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is "self-dialog" in which crowdsourced workers write the entire dialog themselves. We do not restrict the workers to detailed scripts or to a small knowledge base and hence we observe that our dataset contains more realistic and diverse conversations in comparison to existing datasets. We offer several baseline models including state of the art neural seq2seq architectures with benchmark performance as well as qualitative human evaluations. Dialogs are labeled with API calls and arguments, a simple and cost effective approach which avoids the requirement of complex annotation schema. The layer of abstraction between the dialog model and the service provider API allows for a given model to interact with multiple services that provide similar functionally. Finally, the dataset will evoke interest in written vs. spoken language, discourse patterns, error handling and other linguistic phenomena related to dialog system research, development and design.
From Textual Information Sources to Linked Data in the Agatha Project
Authors: Paulo Quaresma, Vitor Beires Nogueira, Kashyap Raiyani, Roy Bayot, Teresa Gonçalves
Date: 2019-09-03 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Semantic Role Labeling

Automatic reasoning about textual information is a challenging task in modern Natural Language Processing (NLP) systems. In this work we describe our proposal for representing and reasoning about Portuguese documents by means of Linked Data like ontologies and thesauri. Our approach resorts to a specialized pipeline of natural language processing (part-of-speech tagger, named entity recognition, semantic role labeling) to populate an ontology for the domain of criminal investigations. The provided architecture and ontology are language independent. Although some of the NLP modules are language dependent, they can be built using adequate AI methodologies.
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction
Authors: Rujun Han, Qiang Ning, Nanyun Peng
Date: 2019-09-02 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Event Extraction, Joint Event and Temporal Relation Extraction, Relation, Relation Extraction, Representation Learning, Structured Prediction, Temporal Relation Extraction

We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. The proposed method has two advantages over existing work. First, it improves event representation by allowing the event and relation modules to share the same contextualized embeddings and neural representation learner. Second, it avoids error propagation in the conventional pipeline systems by leveraging structured inference and learning methods to assign both the event labels and the temporal relation labels jointly. Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark datasets respectively.
Structuring Latent Spaces for Stylized Response Generation
Authors: Xiang Gao, Yizhe Zhang, Sungjin Lee, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan
Date: 2019-09-03 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Response Generation, Style Transfer

Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines.
Identifying and Explaining Discriminative Attributes
Authors: Armins Stepanjans, André Freitas
Date: 2019-09-05 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Attribute, Knowledge Graphs, Natural Language Inference, Relation Extraction

Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-of-the-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.
TransSent: Towards Generation of Structured Sentences with Discourse Marker
Authors: Xing Wu, Dongjun Wei, Liangjun Zang, Jizhong Han, Songlin Hu
Date: 2019-09-05 00:00:00
Tasks: Dialogue Generation, Sentence, Text Generation

Structured sentences are important expressions in human writings and dialogues. Previous works on neural text generation fused semantic and structural information by encoding the entire sentence into a mixed hidden representation. However, when a generated sentence becomes complicated, the structure is difficult to be properly maintained. To alleviate this problem, we explicitly separate the modeling process of semantic and structural information. Intuitively, humans generate structured sentences by directly connecting discourses with discourse markers (such as and, but, etc.). Therefore, we propose a task that mimics this process, called discourse transfer. This task represents a structured sentence as (head discourse, discourse marker, tail discourse), and aims at tail discourse generation based on head discourse and discourse marker. We also propose a corresponding model called TransSent, which interprets the relationship between two discourses as a translation1 from the head discourse to the tail discourse in the embedding space. We experiment TransSent not only in discourse transfer task but also in free text generation and dialogue generation tasks. Automatic and human evaluation results show that TransSent can generate structured sentences with high quality, and has certain scalability in different tasks.
Building Task-Oriented Visual Dialog Systems Through Alternative Optimization Between Dialog Policy and Language Generation
Authors: Mingyang Zhou, Josh Arnold, Zhou Yu
Date: 2019-09-06 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Decoder, Reinforcement Learning, Reinforcement Learning (RL), Text Generation, Visual Dialog

Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence (seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. This paper proposes a novel framework that alternatively trains a RL policy for image guessing and a supervised seq2seq model to improve dialog generation quality. We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality.
Learning Alignment for Multimodal Emotion Recognition from Speech
Authors: Haiyang Xu, HUI ZHANG, Kun Han, Yun Wang, Yiping Peng, Xiangang Li
Date: 2019-09-06 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition

Speech emotion recognition is a challenging problem because human convey emotions in subtle and complex ways. For emotion recognition on human speech, one can either extract emotion related features from audio signals or employ speech recognition techniques to generate text from speech and then apply natural language processing to analyze the sentiment. Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality. One can build models for two input sources separately and combine them in a decision level, but this method ignores the interaction between speech and text in the temporal domain. In this paper, we propose to use an attention mechanism to learn the alignment between speech frames and text words, aiming to produce more accurate multimodal feature representations. The aligned multimodal features are fed into a sequential model for emotion recognition. We evaluate the approach on the IEMOCAP dataset and the experimental results show the proposed approach achieves the state-of-the-art performance on the dataset.
Overton: A Data System for Monitoring and Improving Machine-Learned Products
Authors: Christopher Ré, Feng Niu, Pallavi Gudipati, Charles Srisuwananukorn
Date: 2019-09-07 00:00:00
Tasks: BIG-bench Machine Learning

We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
Quantifying and Correlating Rhythm Formants in Speech
Authors: Dafydd Gibbon, Peng Li
Date: 2019-09-03 00:00:00
Tasks: Rhythm

The objective of the present study is exploratory: to introduce and apply a new theory of speech rhythm zones or rhythm formants (R-formants). R-formants are zones of high magnitude frequencies in the low frequency (LF) long-term spectrum (LTS), rather like formants in the short-term spectra of vowels and consonants. After an illustration of the method, an R-formant analysis is made of non-elicited extracts from public speeches. The LF-LTS of three domains, the amplitude modulated (AM) absolute (rectified) signal, the amplitude envelope modulation (AEM) and frequency modulation (FM, F0, 'pitch') of the signal are compared. The first two correlate well, but the third does not correlate consistently with the other two, presumably due to variability of tone, pitch accent and intonation. Consequently, only the LF LTS of the absolute speech signal is used in the empirical analysis. An informal discussion of the relation between R-formant patterns and utterance structure and a selection of pragmatic variables over the same utterances showed some trends for R-formant functionality and thus useful directions for future research.
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
Authors: Rebekka Burkholz, John Quackenbush
Date: 2019-09-09 00:00:00
Tasks: Clustering

Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation. Related optimization, including influence maximization, model parameter inference, or the development of vaccination strategies, relies heavily on sampling from a model. This is either inefficient or inaccurate. As alternative, we present an efficient message passing algorithm that computes the probability distribution of the cascade size for the Independent Cascade Model on weighted directed networks and generalizations. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well, scales to large networks, and can lead to surprisingly good performance on more dense networks, as we also exemplify on real world data.
Hidden Structure in the Solutions Set of the N Queens Problem
Authors: T. E. Raptis
Date: 2019-07-26 00:00:00

Some preliminary results are reported on the equivalence of any n-queens problem with the roots of a Boolean valued quadratic form via a generic dimensional reduction scheme. It is then proven that the solutions set is encoded in the entries of a special matrix. Further examination reveals a direct association with pointwise Boolean fractal operators applied on certain integer sequences associated with this matrix suggesting the presence of an underlying special geometry of the solutions set.
Robot Capability and Intention in Trust-based Decisions across Tasks
Authors: Yaqi Xie, Indu P Bodala, Desmond C. Ong, David Hsu, Harold Soh
Date: 2019-09-03 00:00:00

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets
Authors: Govind Rathore, Wan-Yi Lin, Ji Eun Kim
Date: 2019-08-29 00:00:00
Tasks: Autonomous Driving, Object, object-detection, Object Detection

Autonomous driving requires various computer vision algorithms, such as object detection and tracking.Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that corrects loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox,we can increase the number of object edges that are labeled automatically (within 1\% error) by 50% to reduce manual annotation time.
Picture What you Read
Authors: Ignazio Gallo, Shah Nawaz, Alessandro Calefati, Riccardo La Grassa, Nicola Landro
Date: 2019-09-09 00:00:00
Tasks: Reading Comprehension

Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension possible. Convolutional Neural Networks (CNN) are an excellent tool for recognizing and classifying text documents. In addition, it can generate images conditioned on natural language. In this work, we utilize CNNs capabilities to generate realistic images representative of the text illustrating the semantic concept. We conducted various experiments to highlight the capacity of the proposed model to generate representative images of the text descriptions used as input to the proposed model.
Multimodal Attention Branch Network for Perspective-Free Sentence Generation
Authors: Aly Magassouba, Komei Sugiura, Hisashi Kawai
Date: 2019-09-10 00:00:00
Tasks: Sentence

In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as "bring me the yellow toy from the upper part of the white shelf" includes referring expressions, i.e., "from the white upper part of the white shelf". To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
Authors: Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, Scott Moura
Date: 2019-09-09 00:00:00
Tasks: Autonomous Driving, Model Predictive Control

This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on heavy traffic where vehicles cannot change lanes without cooperating with other drivers. In this case, classical robust controls may not apply since there is no safe area to merge to without interacting with the other drivers. That said, modeling complex and interactive human behaviors is highly non-trivial from the perspective of control engineers. We propose a mathematical control framework based on Model Predictive Control (MPC) encompassing a state-of-the-art Recurrent Neural network (RNN) architecture. In particular, RNN predicts interactive motions of other drivers in response to potential actions of the autonomous vehicle, which are then systematically evaluated in safety constraints. We also propose a real-time heuristic algorithm to find locally optimal control inputs. Finally, quantitative and qualitative analysis on simulation studies are presented to illustrate the benefits of the proposed framework.