pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations
Authors: Ashwani Bhat, Ashutosh Modi
Date: 2022-11-07 00:00:00
Tasks: Causal Emotion Entailment, Multi-Task Learning

Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.
Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition
Authors: Yashesh Gaur, Nick Kibre, Jian Xue, Kangyuan Shu, Yuhui Wang, Issac Alphanso, Jinyu Li, Yifan Gong
Date: 2022-11-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, TAG, Text Normalization

Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inverse Text Normalization (ITN). In previous works, Weighted Finite State Transducers (WFST) have been employed to do ITN. WFSTs are nicely suited to this task but their size and run-time costs can make deployment on embedded applications challenging. In this paper, we describe the development of an on-device ITN system that is streaming, lightweight & accurate. At the core of our system is a streaming transformer tagger, that tags lexical tokens from ASR. The tag informs which ITN category might be applied, if at all. Following that, we apply an ITN-category-specific WFST, only on the tagged text, to reliably perform the ITN conversion. We show that the proposed ITN solution performs equivalent to strong baselines, while being significantly smaller in size and retaining customization capabilities.
Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
Authors: Huiru Xiao, Xin Liu, Yangqiu Song, Ginny Y. Wong, Simon See
Date: 2022-11-07 00:00:00
Tasks: Knowledge Graph Embeddings

The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to capture the hierarchical patterns due to its tree-like metrics, which addressed the limitations of the Euclidean embedding models. Recent explorations of the complex hyperbolic geometry further improved the hyperbolic embeddings for capturing a variety of hierarchical structures. However, the performance of the hyperbolic KG embedding models for non-transitive relations is still unpromising, while the complex hyperbolic embeddings do not deal with multi-relations. This paper aims to utilize the representation capacity of the complex hyperbolic geometry in multi-relational KG embeddings. To apply the geometric transformations which account for different relations and the attention mechanism in the complex hyperbolic space, we propose to use the fast Fourier transform (FFT) as the conversion between the real and complex hyperbolic space. Constructing the attention-based transformations in the complex space is very challenging, while the proposed Fourier transform-based complex hyperbolic approaches provide a simple and effective solution. Experimental results show that our methods outperform the baselines, including the Euclidean and the real hyperbolic embedding models.
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Authors: Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang
Date: 2022-11-07 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Sentence

Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions. In this paper, we develop a topic-informed discrete latent variable model for semantic textual similarity, which learns a shared latent space for sentence-pair representation via vector quantization. Compared with previous models limited to local semantic contexts, our model can explore richer semantic information via topic modeling. We further boost the performance of semantic similarity by injecting the quantized representation into a transformer-based language model with a well-designed semantic-driven attention mechanism. We demonstrate, through extensive experiments across various English language datasets, that our model is able to surpass several strong neural baselines in semantic textual similarity tasks.
MAISON -- Multimodal AI-based Sensor platform for Older Individuals
Authors: Ali Abedi, Faranak Dayyani, Charlene Chu, Shehroz S. Khan
Date: 2022-11-07 00:00:00

There is a global aging population requiring the need for the right tools that can enable older adults' greater independence and the ability to age at home, as well as assist healthcare workers. It is feasible to achieve this objective by building predictive models that assist healthcare workers in monitoring and analyzing older adults' behavioral, functional, and psychological data. To develop such models, a large amount of multimodal sensor data is typically required. In this paper, we propose MAISON, a scalable cloud-based platform of commercially available smart devices capable of collecting desired multimodal sensor data from older adults and patients living in their own homes. The MAISON platform is novel due to its ability to collect a greater variety of data modalities than the existing platforms, as well as its new features that result in seamless data collection and ease of use for older adults who may not be digitally literate. We demonstrated the feasibility of the MAISON platform with two older adults discharged home from a large rehabilitation center. The results indicate that the MAISON platform was able to collect and store sensor data in a cloud without functional glitches or performance degradation. This paper will also discuss the challenges faced during the development of the platform and data collection in the homes of older adults. MAISON is a novel platform designed to collect multimodal data and facilitate the development of predictive models for detecting key health indicators, including social isolation, depression, and functional decline, and is feasible to use with older adults in the community.
BigCilin: An Automatic Chinese Open-domain Knowledge Graph with Fine-grained Hypernym-Hyponym Relations
Authors: Ming Liu, Yaojia LV, Jingrun Zhang, Ruiji Fu, Bing Qin
Date: 2022-11-07 00:00:00

This paper presents BigCilin, the first Chinese open-domain knowledge graph with fine-grained hypernym-hyponym re-lations which are extracted automatically from multiple sources for Chinese named entities. With the fine-grained hypernym-hyponym relations, BigCilin owns flexible semantic hierarchical structure. Since the hypernym-hyponym paths are automati-cally generated and one entity may have several senses, we provide a path disambi-guation solution to map a hypernym-hyponym path of one entity to its one sense on the condition that the path and the sense express the same meaning. In order to conveniently access our BigCilin Knowle-dge graph, we provide web interface in two ways. One is that it supports querying any Chinese named entity and browsing the extracted hypernym-hyponym paths surro-unding the query entity. The other is that it gives a top-down browsing view to illust-rate the overall hierarchical structure of our BigCilin knowledge graph over some sam-pled entities.
Towards a mathematical understanding of learning from few examples with nonlinear feature maps
Authors: Oliver J. Sutton, Alexander N. Gorban, Ivan Y. Tyukin
Date: 2022-11-07 00:00:00

We consider the problem of data classification where the training set consists of just a few data points. We explore this phenomenon mathematically and reveal key relationships between the geometry of an AI model's feature space, the structure of the underlying data distributions, and the model's generalisation capabilities. The main thrust of our analysis is to reveal the influence on the model's generalisation capabilities of nonlinear feature transformations mapping the original data into high, and possibly infinite, dimensional spaces.
Generalized Product-of-Experts for Learning Multimodal Representations in Noisy Environments
Authors: Abhinav Joshi, Naman Gupta, Jinang Shah, Binod Bhattarai, Ashutosh Modi, Danail Stoyanov
Date: 2022-11-07 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation, Representation Learning, Video Segmentation, Video Semantic Segmentation

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL involves learning reliable and robust representations of information from heterogeneous sources and fusing them. However, in practice, the data acquired from different sources are typically noisy. In some extreme cases, a noise of large magnitude can completely alter the semantics of the data leading to inconsistencies in the parallel multimodal data. In this paper, we propose a novel method for multimodal representation learning in a noisy environment via the generalized product of experts technique. In the proposed method, we train a separate network for each modality to assess the credibility of information coming from that modality, and subsequently, the contribution from each modality is dynamically varied while estimating the joint distribution. We evaluate our method on two challenging benchmarks from two diverse domains: multimodal 3D hand-pose estimation and multimodal surgical video segmentation. We attain state-of-the-art performance on both benchmarks. Our extensive quantitative and qualitative evaluations show the advantages of our method compared to previous approaches.
Spatiotemporal forecasting of vertical track alignment with exogenous factors
Authors: Katsuya Kosukegawa, Yasukuni Mori, Hiroki Suyari, Kazuhiko Kawamoto
Date: 2022-11-07 00:00:00

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory. The proposed method is also experimentally compared with other methods in terms of the forecasting performance. Additionally, an ablation study on exogenous factors is conducted to examine their individual contributions to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of vertical alignment.
Adaptive User-Centered Multimodal Interaction towards Reliable and Trusted Automotive Interfaces
Authors: Amr Gomaa
Date: 2022-11-07 00:00:00
Tasks: multimodal interaction

With the recently increasing capabilities of modern vehicles, novel approaches for interaction emerged that go beyond traditional touch-based and voice command approaches. Therefore, hand gestures, head pose, eye gaze, and speech have been extensively investigated in automotive applications for object selection and referencing. Despite these significant advances, existing approaches mostly employ a one-model-fits-all approach unsuitable for varying user behavior and individual differences. Moreover, current referencing approaches either consider these modalities separately or focus on a stationary situation, whereas the situation in a moving vehicle is highly dynamic and subject to safety-critical constraints. In this paper, I propose a research plan for a user-centered adaptive multimodal fusion approach for referencing external objects from a moving vehicle. The proposed plan aims to provide an open-source framework for user-centered adaptation and personalization using user observations and heuristics, multimodal fusion, clustering, transfer-of-learning for model adaptation, and continuous learning, moving towards trusted human-centered artificial intelligence.
Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces
Authors: Jiahang Cao, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang
Date: 2022-11-07 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this paper, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) Algebraic perspective, (2) Geometric perspective, and (3) Analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.
On the importance of data collection for training general goal-reaching policies
Authors: Alexis Jacq, Manu Orsini, Gabriel Dulac-Arnold, Olivier Pietquin, Matthieu Geist, Olivier Bachem
Date: 2022-11-07 00:00:00
Tasks: continuous-control, Continuous Control

Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist almost unlimited amounts of reasonably coherent data to train from, this is generally not the case for Reinforcement Learning, especially when dealing with a novel environment. In effect, even a relatively trivial continuous environment has an almost limitless number of states, but simply sampling random states and actions will likely not provide transitions that are interesting or useful for any potential downstream task. How should one generate massive amounts of useful data given only an MDP with no indication of downstream tasks? Are the quantity and quality of data truly transformative to the performance of a general controller? We propose to answer both of these questions. First, we introduce a principled unsupervised exploration method, ChronoGEM, which aims to achieve uniform coverage over the manifold of achievable states, which we believe is the most reasonable goal given no prior task information. Secondly, we investigate the effects of both data quantity and data quality on the training of a downstream goal-achievement policy, and show that both large quantities and high-quality of data are essential to train a general controller: a high-precision pose-achievement policy capable of attaining a large number of poses over numerous continuous control embodiments including humanoid.
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
Authors: Gavin Rens, Wen-Chi Yang, Jean-François Raskin, Luc De Raedt
Date: 2022-11-07 00:00:00
Tasks: Relational Reasoning

We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relational setting and combine ideas from relational Markov Decision Processes with pCTL model-checking. More specifically, we assume that there is an unknown relational pCTL target formula that is satisfied by only safe states, and has a horizon of maximum $k$ steps and a threshold probability $\alpha$. The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert. We apply principles of relational learning to induce a pCTL formula that is satisfied by all safe states and none of the unsafe ones. This formula can then be used as a safety specification for this domain, so that the system can avoid getting into dangerous situations in future. Following relational learning principles, we introduce a candidate formula generation process, as well as a method for deciding which candidate formula is a satisfactory specification for the given labeled states. The cases where the expert knows and does not know the system policy are treated, however, much of the learning process is the same for both cases. We evaluate our approach on a synthetic relational domain.
Justification of Recommender Systems Results: A Service-based Approach
Authors: Noemi Mauro, Zhongli Filippo Hu, Liliana Ardissono
Date: 2022-11-07 00:00:00
Tasks: Recommendation Systems

With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are suggested, or why they are relevant, has become a primary goal. However, current models do not explicitly represent the services and actors that the user might encounter during the overall interaction with an item, from its selection to its usage. Thus, they cannot assess their impact on the user's experience. To address this issue, we propose a novel justification approach that uses service models to (i) extract experience data from reviews concerning all the stages of interaction with items, at different granularity levels, and (ii) organize the justification of recommendations around those stages. In a user study, we compared our approach with baselines reflecting the state of the art in the justification of recommender systems results. The participants evaluated the Perceived User Awareness Support provided by our service-based justification models higher than the one offered by the baselines. Moreover, our models received higher Interface Adequacy and Satisfaction evaluations by users having different levels of Curiosity or low Need for Cognition (NfC). Differently, high NfC participants preferred a direct inspection of item reviews. These findings encourage the adoption of service models to justify recommender systems results but suggest the investigation of personalization strategies to suit diverse interaction needs.
Named Entity Recognition in Indian court judgments
Authors: Prathamesh Kalamkar, Astha Agarwal, Aman Tiwari, Smita Gupta, Saurabh Karn, Vivek Raghavan
Date: 2022-11-07 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Identification of named entities from legal texts is an essential building block for developing other legal Artificial Intelligence applications. Named Entities in legal texts are slightly different and more fine-grained than commonly used named entities like Person, Organization, Location etc. In this paper, we introduce a new corpus of 46545 annotated legal named entities mapped to 14 legal entity types. The Baseline model for extracting legal named entities from judgment text is also developed.
Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval
Authors: Donghuo Zeng, Yanan Wang, Jianming Wu, Kazushi Ikeda
Date: 2022-11-07 00:00:00
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval, Triplet

The heterogeneity gap problem is the main challenge in cross-modal retrieval. Because cross-modal data (e.g. audiovisual) have different distributions and representations that cannot be directly compared. To bridge the gap between audiovisual modalities, we learn a common subspace for them by utilizing the intrinsic correlation in the natural synchronization of audio-visual data with the aid of annotated labels. TNN-CCCA is the best audio-visual cross-modal retrieval (AV-CMR) model so far, but the model training is sensitive to hard negative samples when learning common subspace by applying triplet loss to predict the relative distance between inputs. In this paper, to reduce the interference of hard negative samples in representation learning, we propose a new AV-CMR model to optimize semantic features by directly predicting labels and then measuring the intrinsic correlation between audio-visual data using complete cross-triple loss. In particular, our model projects audio-visual features into label space by minimizing the distance between predicted label features after feature projection and ground label representations. Moreover, we adopt complete cross-triplet loss to optimize the predicted label features by leveraging the relationship between all possible similarity and dissimilarity semantic information across modalities. The extensive experimental results on two audio-visual double-checked datasets have shown an improvement of approximately 2.1% in terms of average MAP over the current state-of-the-art method TNN-CCCA for the AV-CMR task, which indicates the effectiveness of our proposed model.
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Authors: Takumi Tanabe, Rei Sato, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
Date: 2022-11-07 00:00:00
Tasks: MuJoCo

In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to the corresponding real-world environment. However, the simulation environment does not perfectly mimic the real-world environment, lead to model misspecification. Multiple studies report significant deterioration of policy performance in a real-world environment. In this study, we focus on scenarios involving a simulation environment with uncertainty parameters and the set of their possible values, called the uncertainty parameter set. The aim is to optimize the worst-case performance on the uncertainty parameter set to guarantee the performance in the corresponding real-world environment. To obtain a policy for the optimization, we propose an off-policy actor-critic approach called the Max-Min Twin Delayed Deep Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min optimization problem using a simultaneous gradient ascent descent approach. Experiments in multi-joint dynamics with contact (MuJoCo) environments show that the proposed method exhibited a worst-case performance superior to several baseline approaches.