pwc-archive/papers-with-abstracts huggingface dataset:
A Quantum Natural Language Processing Approach to Pronoun Resolution
Authors: Hadi Wazni, Kin Ian Lo, Lachlan McPheat, Mehrnoosh Sadrzadeh
Date: 2022-08-10 00:00:00
Tasks: Translation

We use the Lambek Calculus with soft sub-exponential modalities to model and reason about discourse relations such as anaphora and ellipsis. A semantics for this logic is obtained by using truncated Fock spaces, developed in our previous work. We depict these semantic computations via a new string diagram. The Fock Space semantics has the advantage that its terms are learnable from large corpora of data using machine learning and they can be experimented with on mainstream natural language tasks. Further, and thanks to an existing translation from vector spaces to quantum circuits, we can also learn these terms on quantum computers and their simulators, such as the IBMQ range. We extend the existing translation to Fock spaces and develop quantum circuit semantics for discourse relations. We then experiment with the IBMQ AerSimulations of these circuits in a definite pronoun resolution task, where the highest accuracies were recorded for models when the anaphora was resolved.
Paraphrasing, textual entailment, and semantic similarity above word level
Authors: Venelin Kovatchev
Date: 2022-08-10 00:00:00
Tasks: Natural Language Inference, Paraphrase Identification, Semantic Similarity, Semantic Textual Similarity

This dissertation explores the linguistic and computational aspects of the meaning relations that can hold between two or more complex linguistic expressions (phrases, clauses, sentences, paragraphs). In particular, it focuses on Paraphrasing, Textual Entailment, Contradiction, and Semantic Similarity. In Part I: "Similarity at the Level of Words and Phrases", I study the Distributional Hypothesis (DH) and explore several different methodologies for quantifying semantic similarity at the levels of words and short phrases. In Part II: "Paraphrase Typology and Paraphrase Identification", I focus on the meaning relation of paraphrasing and the empirical task of automated Paraphrase Identification (PI). In Part III: "Paraphrasing, Textual Entailment, and Semantic Similarity", I present a novel direction in the research on textual meaning relations, resulting from joint research carried out on on paraphrasing, textual entailment, contradiction, and semantic similarity.
Towards Cross-speaker Reading Style Transfer on Audiobook Dataset
Authors: Xiang Li, Changhe Song, Xianhao Wei, Zhiyong Wu, Jia Jia, Helen Meng
Date: 2022-08-10 00:00:00
Tasks: Style Transfer

Cross-speaker style transfer aims to extract the speech style of the given reference speech, which can be reproduced in the timbre of arbitrary target speakers. Existing methods on this topic have explored utilizing utterance-level style labels to perform style transfer via either global or local scale style representations. However, audiobook datasets are typically characterized by both the local prosody and global genre, and are rarely accompanied by utterance-level style labels. Thus, properly transferring the reading style across different speakers remains a challenging task. This paper aims to introduce a chunk-wise multi-scale cross-speaker style model to capture both the global genre and the local prosody in audiobook speeches. Moreover, by disentangling speaker timbre and style with the proposed switchable adversarial classifiers, the extracted reading style is made adaptable to the timbre of different speakers. Experiment results confirm that the model manages to transfer a given reading style to new target speakers. With the support of local prosody and global genre type predictor, the potentiality of the proposed method in multi-speaker audiobook generation is further revealed.
Proceedings End-to-End Compositional Models of Vector-Based Semantics
Authors: Michael Moortgat, Gijs Wijnholds
Date: 2022-08-10 00:00:00

The workshop End-to-End Compositional Models of Vector-Based Semantics was held at NUI Galway on 15 and 16 August 2022 as part of the 33rd European Summer School in Logic, Language and Information (ESSLLI 2022). The workshop was sponsored by the research project 'A composition calculus for vector-based semantic modelling with a localization for Dutch' (Dutch Research Council 360-89-070, 2017-2022). The workshop program was made up of two parts, the first part reporting on the results of the aforementioned project, the second part consisting of contributed papers on related approaches. The present volume collects the contributed papers and the abstracts of the invited talks.
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?
Authors: Ali Araabi, Christof Monz, Vlad Niculae
Date: 2022-08-10 00:00:00
Proceeding: AMTA 2022 9
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) is an open vocabulary problem. As a result, dealing with the words not occurring during training (a.k.a. out-of-vocabulary (OOV) words) have long been a fundamental challenge for NMT systems. The predominant method to tackle this problem is Byte Pair Encoding (BPE) which splits words, including OOV words, into sub-word segments. BPE has achieved impressive results for a wide range of translation tasks in terms of automatic evaluation metrics. While it is often assumed that by using BPE, NMT systems are capable of handling OOV words, the effectiveness of BPE in translating OOV words has not been explicitly measured. In this paper, we study to what extent BPE is successful in translating OOV words at the word-level. We analyze the translation quality of OOV words based on word type, number of segments, cross-attention weights, and the frequency of segment n-grams in the training data. Our experiments show that while careful BPE settings seem to be fairly useful in translating OOV words across datasets, a considerable percentage of OOV words are translated incorrectly. Furthermore, we highlight the slightly higher effectiveness of BPE in translating OOV words for special cases, such as named-entities and when the languages involved are linguistically close to each other.
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
Authors: Zhijing Wu, Hua Xu, Jingliang Fang, Kai Gao
Date: 2022-08-10 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Domain Adaptation, Incremental Learning, Machine Reading Comprehension, Reading Comprehension

Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings.
TagRec++: Hierarchical Label Aware Attention Network for Question Categorization
Authors: Venktesh Viswanathan, Mukesh Mohania, Vikram Goyal
Date: 2022-08-10 00:00:00
Tasks: Multi-class Classification, Retrieval

Online learning systems have multiple data repositories in the form of transcripts, books and questions. To enable ease of access, such systems organize the content according to a well defined taxonomy of hierarchical nature (subject-chapter-topic). The task of categorizing inputs to the hierarchical labels is usually cast as a flat multi-class classification problem. Such approaches ignore the semantic relatedness between the terms in the input and the tokens in the hierarchical labels. Alternate approaches also suffer from class imbalance when they only consider leaf level nodes as labels. To tackle the issues, we formulate the task as a dense retrieval problem to retrieve the appropriate hierarchical labels for each content. In this paper, we deal with categorizing questions. We model the hierarchical labels as a composition of their tokens and use an efficient cross-attention mechanism to fuse the information with the term representations of the content. We also propose an adaptive in-batch hard negative sampling approach which samples better negatives as the training progresses. We demonstrate that the proposed approach \textit{TagRec++} outperforms existing state-of-the-art approaches on question datasets as measured by Recall@k. In addition, we demonstrate zero-shot capabilities of \textit{TagRec++} and ability to adapt to label changes.
Limitations of Language Models in Arithmetic and Symbolic Induction
Authors: Jing Qian, Hong Wang, Zekun Li, Shiyang Li, Xifeng Yan
Date: 2022-08-09 00:00:00

Recent work has shown that large pretrained Language Models (LMs) can not only perform remarkably well on a range of Natural Language Processing (NLP) tasks but also start improving on reasoning tasks such as arithmetic induction, symbolic manipulation, and commonsense reasoning with increasing size of models. However, it is still unclear what the underlying capabilities of these LMs are. Surprisingly, we find that these models have limitations on certain basic symbolic manipulation tasks such as copy, reverse, and addition. When the total number of symbols or repeating symbols increases, the model performance drops quickly. We investigate the potential causes behind this phenomenon and examine a set of possible methods, including explicit positional markers, fine-grained computation steps, and LMs with callable programs. Experimental results show that none of these techniques can solve the simplest addition induction problem completely. In the end, we introduce LMs with tutor, which demonstrates every single step of teaching. LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.
Natural Language Processing for Systems Engineering: Automatic Generation of Systems Modelling Language Diagrams
Authors: Shaohong Zhong, Andrea Scarinci, Alice Cicirello
Date: 2022-08-09 00:00:00

The design of complex engineering systems is an often long and articulated process that highly relies on engineers' expertise and professional judgment. As such, the typical pitfalls of activities involving the human factor often manifest themselves in terms of lack of completeness or exhaustiveness of the analysis, inconsistencies across design choices or documentation, as well as an implicit degree of subjectivity. An approach is proposed to assist systems engineers in the automatic generation of systems diagrams from unstructured natural language text. Natural Language Processing (NLP) techniques are used to extract entities and their relationships from textual resources (e.g., specifications, manuals, technical reports, maintenance reports) available within an organisation, and convert them into Systems Modelling Language (SysML) diagrams, with particular focus on structure and requirement diagrams. The intention is to provide the users with a more standardised, comprehensive and automated starting point onto which subsequently refine and adapt the diagrams according to their needs. The proposed approach is flexible and open-domain. It consists of six steps which leverage open-access tools, and it leads to an automatic generation of SysML diagrams without intermediate modelling requirement, but through the specification of a set of parameters by the user. The applicability and benefits of the proposed approach are shown through six case studies having different textual sources as inputs, and benchmarked against manually defined diagram elements.
PECCO: A Profit and Cost-oriented Computation Offloading Scheme in Edge-Cloud Environment with Improved Moth-flame Optimisation
Authors: Jiashu Wu, Hao Dai, Yang Wang, Shigen Shen, Chengzhong Xu
Date: 2022-08-09 00:00:00

With the fast growing quantity of data generated by smart devices and the exponential surge of processing demand in the Internet of Things (IoT) era, the resource-rich cloud centres have been utilised to tackle these challenges. To relieve the burden on cloud centres, edge-cloud computation offloading becomes a promising solution since shortening the proximity between the data source and the computation by offloading computation tasks from the cloud to edge devices can improve performance and Quality of Service (QoS). Several optimisation models of edge-cloud computation offloading have been proposed that take computation costs and heterogeneous communication costs into account. However, several important factors are not jointly considered, such as heterogeneities of tasks, load balancing among nodes and the profit yielded by computation tasks, which lead to the profit and cost-oriented computation offloading optimisation model PECCO proposed in this paper. Considering that the model is hard in nature and the optimisation objective is not differentiable, we propose an improved Moth-flame optimiser PECCO-MFI which addresses some deficiencies of the original Moth-flame Optimiser and integrate it under the edge-cloud environment. Comprehensive experiments are conducted to verify the superior performance of the proposed method when optimising the proposed task offloading model under the edge-cloud environment.
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Authors: Siyu Wang, Xiaocong Chen, Lina Yao, Sally Cripps, Julian McAuley
Date: 2022-08-10 00:00:00
Tasks: counterfactual, Data Augmentation, Deep Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning (RL)

Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical with commonly sparse user feedback data in the context of recommender systems. To circumvent the lack of interaction of current RL-based recommender systems, we propose to learn a general Model-Agnostic Counterfactual Synthesis (MACS) Policy for counterfactual user interaction data augmentation. The counterfactual synthesis policy aims to synthesise counterfactual states while preserving significant information in the original state relevant to the user's interests, building upon two different training approaches we designed: learning with expert demonstrations and joint training. As a result, the synthesis of each counterfactual data is based on the current recommendation agent's interaction with the environment to adapt to users' dynamic interests. We integrate the proposed policy Deep Deterministic Policy Gradient (DDPG), Soft Actor Critic (SAC) and Twin Delayed DDPG in an adaptive pipeline with a recommendation agent that can generate counterfactual data to improve the performance of recommendation. The empirical results on both online simulation and offline datasets demonstrate the effectiveness and generalisation of our counterfactual synthesis policy and verify that it improves the performance of RL recommendation agents.
Improving Micro-video Recommendation by Controlling Position Bias
Authors: Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, Wei Zhu
Date: 2022-08-09 00:00:00
Tasks: Contrastive Learning, Position, Sequential Recommendation

As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements.
3D Velocity from 3D Doppler Radial Velocity
Authors: J. L. Barron, 1 R. E. Mercer, 1 X. Chen, 1 P. Joe2
Date: 2005-10-17 00:00:00
Proceeding: Imaging Syst. Technol 2005 10
Tasks: Optical Flow Estimation

We present local least squares and regularization
frameworks for computing 3D velocity (3D optical flow) from 3D radial
velocity measured by a Doppler radar. We demonstrate the performance of our algorithms quantitatively on synthetic radial velocity
data and qualitatively on real radial velocity data, obtained from the
Doppler radar at Kurnell Radar station, Botany Bay, New South
Wales, Australia. Radial velocity can be used to predict the future
positions of storms in sequences of Doppler radar datasets.
LaoNLP: Lao language Natural Language Processing
Authors: Wannaphong Phatthiyaphaibun
Date: 2022-07-22 00:00:00
Proceeding: Zenodo 2022 7

LaoNLP, the Lao language Natural Language Processing Toolkit, is an open-source tool for doing
natural language processing pipelines with the Lao language in Python programming language.
Economics of Semantic Communication System: An Auction Approach
Authors: Zi Qin Liew, Hongyang Du, Wei Yang Bryan Lim, Zehui Xiong, Dusit Niyato, Chunyan Miao, Dong In Kim
Date: 2022-08-02 00:00:00
Tasks: Semantic Communication

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method.
Preserving the beamforming effect for spatial cue-based pseudo-binaural dereverberation of a single source
Authors: Sania Gul, Muhammad Salman Khan, Syed Waqar Shah
Date: 2022-08-10 00:00:00

Reverberations are unavoidable in enclosures, resulting in reduced intelligibility for hearing impaired and non native listeners and even for the normal hearing listeners in noisy circumstances. It also degrades the performance of machine listening applications. In this paper, we propose a novel approach of binaural dereverberation of a single speech source, using the differences in the interaural cues of the direct path signal and the reverberations. Two beamformers, spaced at an interaural distance, are used to extract the reverberations from the reverberant speech. The interaural cues generated by these reverberations and those generated by the direct path signal act as a two class dataset, used for the training of U-Net (a deep convolutional neural network). After its training, the beamformers are removed and the trained U-Net along with the maximum likelihood estimation (MLE) algorithm is used to discriminate between the direct path cues from the reverberation cues, when the system is exposed to the interaural spectrogram of the reverberant speech signal. Our proposed model has outperformed the classical signal processing dereverberation model weighted prediction error in terms of cepstral distance (CEP), frequency weighted segmental signal to noise ratio (FWSEGSNR) and signal to reverberation modulation energy ratio (SRMR) by 1.4 points, 8 dB and 0.6dB. It has achieved better performance than the deep learning based dereverberation model by gaining 1.3 points improvement in CEP with comparable FWSEGSNR, using training dataset which is almost 8 times smaller than required for that model. The proposed model also sustained its performance under relatively similar unseen acoustic conditions and at positions in the vicinity of its training position.
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic
Authors: Christopher Perez, Sayar Karmakar
Date: 2022-07-23 00:00:00
Tasks: Time Series, Time Series Analysis

COVID-19 has brought about many changes in social dynamics. Stay-at-home orders and disruptions in school teaching can influence bullying behavior in-person and online, both of which leading to negative outcomes in victims. To study cyberbullying specifically, 1 million tweets containing keywords associated with abuse were collected from the beginning of 2019 to the end of 2021 with the Twitter API search endpoint. A natural language processing model pre-trained on a Twitter corpus generated probabilities for the tweets being offensive and hateful. To overcome limitations of sampling, data was also collected using the count endpoint. The fraction of tweets from a given daily sample marked as abusive is multiplied to the number reported by the count endpoint. Once these adjusted counts are assembled, a Bayesian autoregressive Poisson model allows one to study the mean trend and lag functions of the data and how they vary over time. The results reveal strong weekly and yearly seasonality in hateful speech but with slight differences across years that may be attributed to COVID-19.