pwc-archive/papers-with-abstracts huggingface dataset:
Improved Synthetic Training for Reading Comprehension
Authors: Yanda Chen, Md Arafat Sultan, Vittorio Castelli
Date: 2020-10-24 00:00:00
Tasks: Knowledge Distillation, Machine Reading Comprehension, Reading Comprehension

Automatically generated synthetic training examples have been shown to improve performance in machine reading comprehension (MRC). Compared to human annotated gold standard data, synthetic training data has unique properties, such as high availability at the possible expense of quality. In view of such differences, in this paper, we explore novel applications of synthetic examples to MRC. Our proposed pre-training and knowledge distillation strategies show significant improvements over existing methods. In a particularly surprising discovery, we observe that synthetic distillation often yields students that can outperform the teacher model.
Structure-Grounded Pretraining for Text-to-SQL
Authors: Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, Oleksandr Polozov, Huan Sun, Matthew Richardson
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Text to SQL, Text-To-SQL

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel prediction tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERT-LARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. The Spider-Realistic dataset is available at https://doi.org/10.5281/zenodo.5205322.
On Learning Text Style Transfer with Direct Rewards
Authors: Yixin Liu, Graham Neubig, John Wieting
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Machine Translation, Semantic Similarity, Semantic Textual Similarity, Style Transfer, Text Style Transfer, Translation

In most cases, the lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. In this paper, we explore training algorithms that instead optimize reward functions that explicitly consider different aspects of the style-transferred outputs. In particular, we leverage semantic similarity metrics originally used for fine-tuning neural machine translation models to explicitly assess the preservation of content between system outputs and input texts. We also investigate the potential weaknesses of the existing automatic metrics and propose efficient strategies of using these metrics for training. The experimental results show that our model provides significant gains in both automatic and human evaluation over strong baselines, indicating the effectiveness of our proposed methods and training strategies.
Conversational Semantic Parsing for Dialog State Tracking
Authors: Jianpeng Cheng, Devang Agrawal, Hector Martinez Alonso, Shruti Bhargava, Joris Driesen, Federico Flego, Shaona Ghosh, Dain Kaplan, Dimitri Kartsaklis, Lin Li, Dhivya Piraviperumal, Jason D Williams, Hong Yu, Diarmuid O Seaghdha, Anders Johannsen
Date: 2020-10-24 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Decoder, dialog state tracking, Semantic Parsing

We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositionality, cross-domain knowledge sharing and co-reference. We present TreeDST, a dataset of 27k conversations annotated with tree-structured dialog states and system acts. We describe an encoder-decoder framework for DST with hierarchical representations, which leads to 20% improvement over state-of-the-art DST approaches that operate on a flat meaning space of slot-value pairs.
Modular Networks for Compositional Instruction Following
Authors: Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, Trevor Darrell
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Instruction Following

Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
Federated Bandit: A Gossiping Approach
Authors: Zhaowei Zhu, Jingxuan Zhu, Ji Liu, Yang Liu
Date: 2020-10-24 00:00:00
Tasks: Federated Learning

In this paper, we study \emph{Federated Bandit}, a decentralized Multi-Armed Bandit problem with a set of $N$ agents, who can only communicate their local data with neighbors described by a connected graph $G$. Each agent makes a sequence of decisions on selecting an arm from $M$ candidates, yet they only have access to local and potentially biased feedback/evaluation of the true reward for each action taken. Learning only locally will lead agents to sub-optimal actions while converging to a no-regret strategy requires a collection of distributed data. Motivated by the proposal of federated learning, we aim for a solution with which agents will never share their local observations with a central entity, and will be allowed to only share a private copy of his/her own information with their neighbors. We first propose a decentralized bandit algorithm Gossip_UCB, which is a coupling of variants of both the classical gossiping algorithm and the celebrated Upper Confidence Bound (UCB) bandit algorithm. We show that Gossip_UCB successfully adapts local bandit learning into a global gossiping process for sharing information among connected agents, and achieves guaranteed regret at the order of $O(\max\{ \texttt{poly}(N,M) \log T, \texttt{poly}(N,M)\log_{\lambda_2^{-1}} N\})$ for all $N$ agents, where $\lambda_2\in(0,1)$ is the second largest eigenvalue of the expected gossip matrix, which is a function of $G$. We then propose Fed_UCB, a differentially private version of Gossip_UCB, in which the agents preserve $\epsilon$-differential privacy of their local data while achieving $O(\max \{\frac{\texttt{poly}(N,M)}{\epsilon}\log^{2.5} T, \texttt{poly}(N,M) (\log_{\lambda_2^{-1}} N + \log T) \})$ regret.
Measuring Association Between Labels and Free-Text Rationales
Authors: Sarah Wiegreffe, Ana Marasović, Noah A. Smith
Date: 2020-10-24 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Decision Making, Feature Importance, Natural Language Inference, Question Answering

In interpretable NLP, we require faithful rationales that reflect the model's decision-making process for an explained instance. While prior work focuses on extractive rationales (a subset of the input words), we investigate their less-studied counterpart: free-text natural language rationales. We demonstrate that pipelines, existing models for faithful extractive rationalization on information-extraction style tasks, do not extend as reliably to "reasoning" tasks requiring free-text rationales. We turn to models that jointly predict and rationalize, a class of widely used high-performance models for free-text rationalization whose faithfulness is not yet established. We define label-rationale association as a necessary property for faithfulness: the internal mechanisms of the model producing the label and the rationale must be meaningfully correlated. We propose two measurements to test this property: robustness equivalence and feature importance agreement. We find that state-of-the-art T5-based joint models exhibit both properties for rationalizing commonsense question-answering and natural language inference, indicating their potential for producing faithful free-text rationales.
Dataset Dynamics via Gradient Flows in Probability Space
Authors: David Alvarez-Melis, Nicolò Fusi
Date: 2020-10-24 00:00:00
Tasks: BIG-bench Machine Learning, Domain Adaptation, Transfer Learning

Various machine learning tasks, from generative modeling to domain adaptation, revolve around the concept of dataset transformation and manipulation. While various methods exist for transforming unlabeled datasets, principled methods to do so for labeled (e.g., classification) datasets are missing. In this work, we propose a novel framework for dataset transformation, which we cast as optimization over data-generating joint probability distributions. We approach this class of problems through Wasserstein gradient flows in probability space, and derive practical and efficient particle-based methods for a flexible but well-behaved class of objective functions. Through various experiments, we show that this framework can be used to impose constraints on classification datasets, adapt them for transfer learning, or to re-purpose fixed or black-box models to classify ---with high accuracy--- previously unseen datasets.
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions
Authors: Zhiyu Chen, Honglei Liu, Hu Xu, Seungwhan Moon, Hao Zhou, Bing Liu
Date: 2020-10-24 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Conversational Recommendation, Dialogue State Tracking

Existing conversational systems are mostly agent-centric, which assumes the user utterances would closely follow the system ontology (for NLU or dialogue state tracking). However, in real-world scenarios, it is highly desirable that the users can speak freely in their own way. It is extremely hard, if not impossible, for the users to adapt to the unknown system ontology. In this work, we attempt to build a user-centric dialogue system. As there is no clean mapping for a user's free form utterance to an ontology, we first model the user preferences as estimated distributions over the system ontology and map the users' utterances to such distributions. Learning such a mapping poses new challenges on reasoning over existing knowledge, ranging from factoid knowledge, commonsense knowledge to the users' own situations. To this end, we build a new dataset named NUANCED that focuses on such realistic settings for conversational recommendation. Collected via dialogue simulation and paraphrasing, NUANCED contains 5.1k dialogues, 26k turns of high-quality user responses. We conduct experiments, showing both the usefulness and challenges of our problem setting. We believe NUANCED can serve as a valuable resource to push existing research from the agent-centric system to the user-centric system. The code and data is publicly available at \url{https://github.com/facebookresearch/nuanced}.
Adding Chit-Chat to Enhance Task-Oriented Dialogues
Authors: Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Dialogue Generation, Dialogue Understanding, Task-Oriented Dialogue Systems

Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e.g., booking hotels), open-domain chatbots aim at making socially engaging conversations. In this work, we propose to integrate both types of systems by Adding Chit-Chat to ENhance Task-ORiented dialogues (ACCENTOR), with the goal of making virtual assistant conversations more engaging and interactive. Specifically, we propose a Human <-> AI collaborative data collection approach for generating diverse chit-chat responses to augment task-oriented dialogues with minimal annotation effort. We then present our new chit-chat-based annotations to 23.8K dialogues from two popular task-oriented datasets (Schema-Guided Dialogue and MultiWOZ 2.1) and demonstrate their advantage over the originals via human evaluation. Lastly, we propose three new models for adding chit-chat to task-oriented dialogues, explicitly trained to predict user goals and to generate contextually relevant chit-chat responses. Automatic and human evaluations show that, compared with the state-of-the-art task-oriented baseline, our models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike, while maintaining competitive task performance.
Effective Distant Supervision for Temporal Relation Extraction
Authors: Xinyu Zhao, Shih-ting Lin, Greg Durrett
Date: 2020-10-24 00:00:00
Proceeding: EACL (AdaptNLP) 2021 4
Tasks: Relation, Relation Extraction, Temporal Relation Extraction

A principal barrier to training temporal relation extraction models in new domains is the lack of varied, high quality examples and the challenge of collecting more. We present a method of automatically collecting distantly-supervised examples of temporal relations. We scrape and automatically label event pairs where the temporal relations are made explicit in text, then mask out those explicit cues, forcing a model trained on this data to learn other signals. We demonstrate that a pre-trained Transformer model is able to transfer from the weakly labeled examples to human-annotated benchmarks in both zero-shot and few-shot settings, and that the masking scheme is important in improving generalization.
Autoencoder Watchdog Outlier Detection for Classifiers
Authors: Justin Bui, Robert J Marks II
Date: 2020-10-24 00:00:00
Tasks: Outlier Detection, regression

Neural networks have often been described as black boxes. A generic neural network trained to differentiate between kittens and puppies will classify a picture of a kumquat as a kitten or a puppy. An autoencoder watch dog screens trained classifier/regression machine input candidates before processing, e.g. to first test whether the neural network input is a puppy or a kitten. Preliminary results are presented using convolutional neural networks and convolutional autoencoder watchdogs using MNIST images.
Temporal Reasoning on Implicit Events from Distant Supervision
Authors: Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth
Date: 2020-10-24 00:00:00
Proceeding: NAACL 2021 4
Tasks: Natural Language Inference

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it. This introduces a new challenge in temporal reasoning research, where prior work has focused on explicitly mentioned events. Human readers can infer implicit events via commonsense reasoning, resulting in a more comprehensive understanding of the situation and, consequently, better reasoning about time. We find, however, that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. To address this, we propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to combine start times and durations to infer end times. SYMTIME outperforms strong baseline systems on TRACIE by 5%, and by 11% in a zero prior knowledge training setting. Our approach also generalizes to other temporal reasoning tasks, as evidenced by a gain of 1%-9% on MATRES, an explicit event benchmark.
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization
Authors: Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan
Date: 2020-10-24 00:00:00
Tasks: Anomaly Detection, Model extraction

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, prior works focus on the models trained over the Euclidean space, e.g., images and texts, while how to extract a GNN model that contains a graph structure and node features is yet to be explored. In this paper, for the first time, we comprehensively investigate and develop model extraction attacks against GNN models. We first systematically formalise the threat modelling in the context of GNN model extraction and classify the adversarial threats into seven categories by considering different background knowledge of the attacker, e.g., attributes and/or neighbour connections of the nodes obtained by the attacker. Then we present detailed methods which utilise the accessible knowledge in each threat to implement the attacks. By evaluating over three real-world datasets, our attacks are shown to extract duplicated models effectively, i.e., 84% - 89% of the inputs in the target domain have the same output predictions as the victim model.
Scale-, shift- and rotation-invariant diffractive optical networks
Authors: Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Date: 2020-10-24 00:00:00
Tasks: image-classification, Image Classification, Translation

Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces, designed using deep learning, to compute a desired statistical inference task as the light waves propagate from the input plane to the output field-of-view. Although, earlier studies have demonstrated the generalization capability of diffractive optical networks to unseen data, achieving e.g., >98% image classification accuracy for handwritten digits, these previous designs are in general sensitive to the spatial scaling, translation and rotation of the input objects. Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase as uniformly distributed random variables to build resilience in their blind inference performance against such object transformations. This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant, which is especially important and useful for dynamic machine vision applications in e.g., autonomous cars, in-vivo imaging of biomedical specimen, among others.
Text Style Transfer: A Review and Experimental Evaluation
Authors: Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal, Aston Zhang
Date: 2020-10-24 00:00:00
Tasks: Style Transfer, Text Style Transfer

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has burgeoned because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models and provide a comprehensive summary of the state of the art. We review the existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study where we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.
An Evaluation Protocol for Generative Conversational Systems
Authors: Seolhwa Lee, Heuiseok Lim, João Sedoc
Date: 2020-10-24 00:00:00
Tasks: Experimental Design

There is a multitude of novel generative models for open-domain conversational systems; however, there is no systematic evaluation of different systems. Systematic comparisons require consistency in experimental design, evaluation sets, conversational systems and their outputs, and statistical analysis. We lay out a protocol for the evaluation of conversational models using head-to-head pairwise comparison. We analyze ten recent models that claim state-of-the-art performance using a paired head-to-head performance (win-loss-tie) on five evaluation datasets. Our findings show that DialoGPT and Blender are superior systems using Bradley-Terry model and TrueSkill ranking methods. These findings demonstrate the feasibility of our protocol to evaluate conversational agents and evaluation sets. Finally, we make all code and evaluations publicly available for researchers to compare their model to other state-of-the-art dialog models.
Real-time Non-line-of-Sight imaging of dynamic scenes
Authors: Ji Hyun Nam, Eric Brandt, Sebastian Bauer, Xiaochun Liu, Eftychios Sifakis, Andreas Velten
Date: 2020-10-24 00:00:00
Tasks: 3D geometry

Non-Line-of-Sight (NLOS) imaging aims at recovering the 3D geometry of objects that are hidden from the direct line of sight. In the past, this method has suffered from the weak available multibounce signal limiting scene size, capture speed, and reconstruction quality. While algorithms capable of reconstructing scenes at several frames per second have been demonstrated, real-time NLOS video has only been demonstrated for retro-reflective objects where the NLOS signal strength is enhanced by 4 orders of magnitude or more. Furthermore, it has also been noted that the signal-to-noise ratio of reconstructions in NLOS methods drops quickly with distance and past reconstructions, therefore, have been limited to small scenes with depths of few meters. Actual models of noise and resolution in the scene have been simplistic, ignoring many of the complexities of the problem. We show that SPAD (Single-Photon Avalanche Diode) array detectors with a total of just 28 pixels combined with a specifically extended Phasor Field reconstruction algorithm can reconstruct live real-time videos of non-retro-reflective NLOS scenes. We provide an analysis of the Signal-to-Noise-Ratio (SNR) of our reconstructions and show that for our method it is possible to reconstruct the scene such that SNR, motion blur, angular resolution, and depth resolution are all independent of scene size suggesting that reconstruction of very large scenes may be possible. In the future, the light efficiency for NLOS imaging systems can be improved further by adding more pixels to the sensor array.
A Comparison of Discrete Latent Variable Models for Speech Representation Learning
Authors: Henry Zhou, Alexei Baevski, Michael Auli
Date: 2020-10-24 00:00:00
Tasks: Phoneme Recognition, Representation Learning, Speech Representation Learning

Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery and phoneme recognition performance. Results show that future time-step prediction with vq-wav2vec achieves better performance. The best system achieves an error rate of 13.22 on the ZeroSpeech 2019 ABX phoneme discrimination challenge