pwc-archive/papers-with-abstracts huggingface dataset:
Question-Driven Design Process for Explainable AI User Experiences
Authors: Q. Vera Liao, Milena Pribić, Jaesik Han, Sarah Miller, Daby Sow
Date: 2021-04-08 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

A pervasive design issue of AI systems is their explainability--how to provide appropriate information to help users understand the AI. The technical field of explainable AI (XAI) has produced a rich toolbox of techniques. Designers are now tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions. Informed by our previous work studying design challenges around XAI UX, this work proposes a design process to tackle these challenges. We review our and related prior work to identify requirements that the process should fulfill, and accordingly, propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions. We provide a mapping guide between prototypical user questions and exemplars of XAI techniques to reframe the technical space of XAI, also serving as boundary objects to support collaboration between designers and AI engineers. We demonstrate it with a use case of designing XAI for healthcare adverse events prediction, and discuss lessons learned for tackling design challenges of AI systems.
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
Authors: Szu-Wei Fu, Cheng Yu, Tsun-An Hsieh, Peter Plantinga, Mirco Ravanelli, Xugang Lu, Yu Tsao
Date: 2021-04-08 00:00:00
Tasks: Speech Enhancement

The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quality of enhanced speech unsatisfactory. Objective evaluation metrics which consider human perception can hence serve as a bridge to reduce the gap. Our previously proposed MetricGAN was designed to optimize objective metrics by connecting the metric with a discriminator. Because only the scores of the target evaluation functions are needed during training, the metrics can even be non-differentiable. In this study, we propose a MetricGAN+ in which three training techniques incorporating domain-knowledge of speech processing are proposed. With these techniques, experimental results on the VoiceBank-DEMAND dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).
Extended Parallel Corpus for Amharic-English Machine Translation
Authors: Andargachew Mekonnen Gezmu, Andreas Nürnberger, Tesfaye Bayu Bati
Date: 2021-04-08 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine translation systems; we trained statistical and neural machine translation models using the corpus. In the experiments, we also used a large monolingual corpus for the language model of statistical machine translation and back-translation of neural machine translation. In the automatic evaluation, neural machine translation models outperform statistical machine translation models by approximately six to seven Bilingual Evaluation Understudy (BLEU) points. Besides, among the neural machine translation models, the subword models outperform the word-based models by three to four BLEU points. Moreover, two other relevant automatic evaluation metrics, Translation Edit Rate on Character Level and Better Evaluation as Ranking, reflect corresponding differences among the trained models.
Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems
Authors: Linh Kästner, Teham Buiyan, Xinlin Zhao, Lei Jiao, Zhengcheng Shen, Jens Lambrecht
Date: 2021-04-08 00:00:00
Tasks: Autonomous Navigation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learning approaches are not suitable for long-range navigation due to their proneness to local minima and lack of long term memory, which hinders its widespread integration into industrial applications of mobile robotics. In this paper, we propose a navigation system incorporating deep-reinforcement-learning-based local planners into conventional navigation stacks for long-range navigation. Therefore, a framework for training and testing the deep reinforcement learning algorithms along with classic approaches is presented. We evaluated our deep-reinforcement-learning-enhanced navigation system against various conventional planners and found that our system outperforms them in terms of safety, efficiency and robustness.
Half-Truth: A Partially Fake Audio Detection Dataset
Authors: Jiangyan Yi, Ye Bai, JianHua Tao, Haoxin Ma, Zhengkun Tian, Chenglong Wang, Tao Wang, Ruibo Fu
Date: 2021-04-08 00:00:00
Tasks: Speech Synthesis

Diverse promising datasets have been designed to hold back the development of fake audio detection, such as ASVspoof databases. However, previous datasets ignore an attacking situation, in which the hacker hides some small fake clips in real speech audio. This poses a serious threat since that it is difficult to distinguish the small fake clip from the whole speech utterance. Therefore, this paper develops such a dataset for half-truth audio detection (HAD). Partially fake audio in the HAD dataset involves only changing a few words in an utterance.The audio of the words is generated with the very latest state-of-the-art speech synthesis technology. We can not only detect fake uttrances but also localize manipulated regions in a speech using this dataset. Some benchmark results are presented on this dataset. The results show that partially fake audio presents much more challenging than fully fake audio for fake audio detection. The HAD dataset is publicly available: https://zenodo.org/records/10377492.
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators
Authors: Linh Kästner, Teham Buiyan, Xinlin Zhao, Zhengcheng Shen, Cornelius Marx, Jens Lambrecht
Date: 2021-04-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, the integration of Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obstacle avoidance with conventional global planning methods using waypoint generation. Therefore, we integrate different waypoint generators into existing navigation systems and compare the joint system against traditional ones. We found an increased performance in terms of safety, efficiency and path smoothness especially in highly dynamic environments.
Computation and Bribery of Voting Power in Delegative Simple Games
Authors: Gianlorenzo D'Angelo, Esmaeil Delfaraz, Hugo Gilbert
Date: 2021-04-08 00:00:00

Following Zhang and Grossi~(AAAI 2021), we study in more depth a variant of weighted voting games in which agents' weights are induced by a transitive support structure. This class of simple games is notably well suited to study the relative importance of agents in the liquid democracy framework. We first propose a pseudo-polynomial time algorithm to compute the Banzhaf and Shapley-Shubik indices for this class of game. Then, we study a bribery problem, in which one tries to maximize/minimize the voting power/weight of a given agent by changing the support structure under a budget constraint. We show that these problems are computationally hard and provide several parameterized complexity results.
Unsupervised Speech Representation Learning for Behavior Modeling using Triplet Enhanced Contextualized Networks
Authors: Haoqi Li, Brian Baucom, Shrikanth Narayanan, Panayiotis Georgiou
Date: 2021-04-01 00:00:00
Tasks: Decoder, Representation Learning, Speech Representation Learning, Triplet

Speech encodes a wealth of information related to human behavior and has been used in a variety of automated behavior recognition tasks. However, extracting behavioral information from speech remains challenging including due to inadequate training data resources stemming from the often low occurrence frequencies of specific behavioral patterns. Moreover, supervised behavioral modeling typically relies on domain-specific construct definitions and corresponding manually-annotated data, rendering generalizing across domains challenging. In this paper, we exploit the stationary properties of human behavior within an interaction and present a representation learning method to capture behavioral information from speech in an unsupervised way. We hypothesize that nearby segments of speech share the same behavioral context and hence map onto similar underlying behavioral representations. We present an encoder-decoder based Deep Contextualized Network (DCN) as well as a Triplet-Enhanced DCN (TE-DCN) framework to capture the behavioral context and derive a manifold representation, where speech frames with similar behaviors are closer while frames of different behaviors maintain larger distances. The models are trained on movie audio data and validated on diverse domains including on a couples therapy corpus and other publicly collected data (e.g., stand-up comedy). With encouraging results, our proposed framework shows the feasibility of unsupervised learning within cross-domain behavioral modeling.
Finding Experts in Social Media Data using a Hybrid Approach
Authors: Simon James, Brady
Date: 2021-04-01 00:00:00

Several approaches to the problem of expert finding have emerged in computer science research. In this work, three of these approaches - content analysis, social graph analysis and the use of Semantic Web technologies are examined. An integrated set of system requirements is then developed that uses all three approaches in one hybrid approach. To show the practicality of this hybrid approach, a usable prototype expert finding system called ExpertQuest is developed using a modern functional programming language (Clojure) to query social media data and Linked Data. This system is evaluated and discussed. Finally, a discussion and conclusions are presented which describe the benefits and shortcomings of the hybrid approach and the technologies used in this work.
Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization
Authors: Rezvaneh Rezapour, Sravana Reddy, Ann Clifton, Rosie Jones
Date: 2021-04-07 00:00:00
Tasks: Retrieval, Text Retrieval

This paper contains the description of our submissions to the summarization task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The goal of this challenge was to generate short, informative summaries that contain the key information present in a podcast episode using automatically generated transcripts of the podcast audio. Since podcasts vary with respect to their genre, topic, and granularity of information, we propose two summarization models that explicitly take genre and named entities into consideration in order to generate summaries appropriate to the style of the podcasts. Our models are abstractive, and supervised using creator-provided descriptions as ground truth summaries. The results of the submitted summaries show that our best model achieves an aggregate quality score of 1.58 in comparison to the creator descriptions and a baseline abstractive system which both score 1.49 (an improvement of 9%) as assessed by human evaluators.
EXPATS: A Toolkit for Explainable Automated Text Scoring
Authors: Hitoshi Manabe, Masato Hagiwara
Date: 2021-04-07 00:00:00
Tasks: Automated Essay Scoring

Automated text scoring (ATS) tasks, such as automated essay scoring and readability assessment, are important educational applications of natural language processing. Due to their interpretability of models and predictions, traditional machine learning (ML) algorithms based on handcrafted features are still in wide use for ATS tasks. Practitioners often need to experiment with a variety of models (including deep and traditional ML ones), features, and training objectives (regression and classification), although modern deep learning frameworks such as PyTorch require deep ML expertise to fully utilize. In this paper, we present EXPATS, an open-source framework to allow its users to develop and experiment with different ATS models quickly by offering flexible components, an easy-to-use configuration system, and the command-line interface. The toolkit also provides seamless integration with the Language Interpretability Tool (LIT) so that one can interpret and visualize models and their predictions. We also describe two case studies where we build ATS models quickly with minimal engineering efforts. The toolkit is available at \url{https://github.com/octanove/expats}.
Interpreting Verbal Metaphors by Paraphrasing
Authors: Rui Mao, Chenghua Lin, Frank Guerin
Date: 2021-04-07 00:00:00
Tasks: Machine Translation, Translation

Metaphorical expressions are difficult linguistic phenomena, challenging diverse Natural Language Processing tasks. Previous works showed that paraphrasing a metaphor as its literal counterpart can help machines better process metaphors on downstream tasks. In this paper, we interpret metaphors with BERT and WordNet hypernyms and synonyms in an unsupervised manner, showing that our method significantly outperforms the state-of-the-art baseline. We also demonstrate that our method can help a machine translation system improve its accuracy in translating English metaphors to 8 target languages.
Nutribullets Hybrid: Multi-document Health Summarization
Authors: Darsh J Shah, Lili Yu, Tao Lei, Regina Barzilay
Date: 2021-04-08 00:00:00
Tasks: Language Modeling, Language Modelling, Nutrition, Text Infilling

We present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health -- rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization -- while being equally fluent.
Revisiting Simple Neural Probabilistic Language Models
Authors: Simeng Sun, Mohit Iyyer
Date: 2021-04-08 00:00:00
Proceeding: NAACL 2021 4
Tasks: Language Modeling, Language Modelling, Word Embeddings

Recent progress in language modeling has been driven not only by advances in neural architectures, but also through hardware and optimization improvements. In this paper, we revisit the neural probabilistic language model (NPLM) of~\citet{Bengio2003ANP}, which simply concatenates word embeddings within a fixed window and passes the result through a feed-forward network to predict the next word. When scaled up to modern hardware, this model (despite its many limitations) performs much better than expected on word-level language model benchmarks. Our analysis reveals that the NPLM achieves lower perplexity than a baseline Transformer with short input contexts but struggles to handle long-term dependencies. Inspired by this result, we modify the Transformer by replacing its first self-attention layer with the NPLM's local concatenation layer, which results in small but consistent perplexity decreases across three word-level language modeling datasets.
Low-Complexity Probing via Finding Subnetworks
Authors: Steven Cao, Victor Sanh, Alexander M. Rush
Date: 2021-04-08 00:00:00
Proceeding: NAACL 2021 4

The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model's internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.
User-Generated Text Corpus for Evaluating Japanese Morphological Analysis and Lexical Normalization
Authors: Shohei Higashiyama, Masao Utiyama, Taro Watanabe, Eiichiro Sumita
Date: 2021-04-08 00:00:00
Proceeding: NAACL 2021 4
Tasks: Lexical Normalization, Morphological Analysis

Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comprises 929 sentences annotated with morphological and normalization information, along with category information we classified for frequent UGT-specific phenomena. Experiments on the corpus demonstrated the low performance of existing MA/LN methods for non-general words and non-standard forms, indicating that the corpus would be a challenging benchmark for further research on UGT.
BSTC: A Large-Scale Chinese-English Speech Translation Dataset
Authors: Ruiqing Zhang, Xiyang Wang, Chuanqiang Zhang, Zhongjun He, Hua Wu, Zhi Li, Haifeng Wang, Ying Chen, Qinfei Li
Date: 2021-04-08 00:00:00
Proceeding: NAACL (AutoSimTrans) 2021 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Translation

This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-English speech translation dataset. This dataset is constructed based on a collection of licensed videos of talks or lectures, including about 68 hours of Mandarin data, their manual transcripts and translations into English, as well as automated transcripts by an automatic speech recognition (ASR) model. We have further asked three experienced interpreters to simultaneously interpret the testing talks in a mock conference setting. This corpus is expected to promote the research of automatic simultaneous translation as well as the development of practical systems. We have organized simultaneous translation tasks and used this corpus to evaluate automatic simultaneous translation systems.
Who Should Go First? A Self-Supervised Concept Sorting Model for Improving Taxonomy Expansion
Authors: Xiangchen Song, Jiaming Shen, Jieyu Zhang, Jiawei Han
Date: 2021-04-08 00:00:00
Tasks: Taxonomy Expansion

Taxonomies have been widely used in various machine learning and text mining systems to organize knowledge and facilitate downstream tasks. One critical challenge is that, as data and business scope grow in real applications, existing taxonomies need to be expanded to incorporate new concepts. Previous works on taxonomy expansion process the new concepts independently and simultaneously, ignoring the potential relationships among them and the appropriate order of inserting operations. However, in reality, the new concepts tend to be mutually correlated and form local hypernym-hyponym structures. In such a scenario, ignoring the dependencies of new concepts and the order of insertion may trigger error propagation. For example, existing taxonomy expansion systems may insert hyponyms to existing taxonomies before their hypernym, leading to sub-optimal expanded taxonomies. To complement existing taxonomy expansion systems, we propose TaxoOrder, a novel self-supervised framework that simultaneously discovers the local hypernym-hyponym structure among new concepts and decides the order of insertion. TaxoOrder can be directly plugged into any taxonomy expansion system and improve the quality of expanded taxonomies. Experiments on the real-world dataset validate the effectiveness of TaxoOrder to enhance taxonomy expansion systems, leading to better-resulting taxonomies with comparison to baselines under various evaluation metrics.
Languages for Smart and Computable Contracts
Authors: Christopher D. Clack
Date: 2021-04-08 00:00:00

Smart Contracts use computer technology to automate the performance of aspects of commercial agreements. Yet how can there be confidence that the computer code is faithful to the intentions of the parties? To understand the depth and subtlety of this question requires an exploration of natural and computer languages, of the semantics of expressions in those languages, and of the gap that exists between the disciplines of law and computer science. Here we provide a perspective on some of the key issues, explore some current research directions, and explain the importance of language design in the development of reliable Smart Contracts, including the specific methodology of Computable Contracts.
Uppsala NLP at SemEval-2021 Task 2: Multilingual Language Models for Fine-tuning and Feature Extraction in Word-in-Context Disambiguation
Authors: Huiling You, Xingran Zhu, Sara Stymne
Date: 2021-04-08 00:00:00
Proceeding: SEMEVAL 2021
Tasks: Task 2

We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation. We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT) and multilingual distilled BERT (mDistilBERT). We compare these three models in two setups, fine-tuning and as feature extractors. In the second case we also experiment with using dependency-based information. We find that fine-tuning is better than feature extraction. XLMR performs better than mBERT in the cross-lingual setting both with fine-tuning and feature extraction, whereas these two models give a similar performance in the multilingual setting. mDistilBERT performs poorly with fine-tuning but gives similar results to the other models when used as a feature extractor. We submitted our two best systems, fine-tuned with XLMR and mBERT.