pwc-archive/papers-with-abstracts huggingface dataset:
Empathetic Dialogue Generation with Pre-trained RoBERTa-GPT2 and External Knowledge
Authors: Ye Liu, Wolfgang Maier, Wolfgang Minker, Stefan Ultes
Date: 2021-09-07 00:00:00
Tasks: Decoder, Dialogue Generation

One challenge for dialogue agents is to recognize feelings of the conversation partner and respond accordingly. In this work, RoBERTa-GPT2 is proposed for empathetic dialogue generation, where the pre-trained auto-encoding RoBERTa is utilised as encoder and the pre-trained auto-regressive GPT-2 as decoder. With the combination of the pre-trained RoBERTa and GPT-2, our model realizes a new state-of-the-art emotion accuracy. To enable the empathetic ability of RoBERTa-GPT2 model, we propose a commonsense knowledge and emotional concepts extractor, in which the commonsensible and emotional concepts of dialogue context are extracted for the GPT-2 decoder. The experiment results demonstrate that the empathetic dialogue generation benefits from both pre-trained encoder-decoder architecture and external knowledge.
Generate & Rank: A Multi-task Framework for Math Word Problems
Authors: Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, Qun Liu
Date: 2021-09-07 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Language Modeling, Language Modelling, Math, Math Word Problem Solving

Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% $\rightarrow$ 85.4%) higher than the state-of-the-art.
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
Authors: Zeyang Liu, Ke Zhou, Jiaxin Mao, Max L. Wilson
Date: 2021-09-07 00:00:00
Tasks: Conversational Search, POS

Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems. Evaluating such systems is very challenging since search results are presented in the format of natural language sentences. Given the unlimited number of possible responses, collecting relevance assessments for all the possible responses is infeasible. In this paper, we propose POSSCORE, a simple yet effective automatic evaluation method for conversational search. The proposed embedding-based metric takes the influence of part of speech (POS) of the terms in the response into account. To the best knowledge, our work is the first to systematically demonstrate the importance of incorporating syntactic information, such as POS labels, for conversational search evaluation. Experimental results demonstrate that our metrics can correlate with human preference, achieving significant improvements over state-of-the-art baseline metrics.
Reconfigurable co-processor architecture with limited numerical precision to accelerate deep convolutional neural networks
Authors: Sasindu Wijeratne, Sandaruwan Jayaweera, Mahesh Dananjaya, Ajith Pasqual
Date: 2021-08-21 00:00:00
Tasks: Quantization

Convolutional Neural Networks (CNNs) are widely used in deep learning applications, e.g. visual systems, robotics etc. However, existing software solutions are not efficient. Therefore, many hardware accelerators have been proposed optimizing performance, power and resource utilization of the implementation. Amongst existing solutions, Field Programmable Gate Array (FPGA) based architecture provides better cost-energy-performance trade-offs as well as scalability and minimizing development time. In this paper, we present a model-independent reconfigurable co-processing architecture to accelerate CNNs. Our architecture consists of parallel Multiply and Accumulate (MAC) units with caching techniques and interconnection networks to exploit maximum data parallelism. In contrast to existing solutions, we introduce limited precision 32 bit Q-format fixed point quantization for arithmetic representations and operations. As a result, our architecture achieved significant reduction in resource utilization with competitive accuracy. Furthermore, we developed an assembly-type microinstructions to access the co-processing fabric to manage layer-wise parallelism, thereby making re-use of limited resources. Finally, we have tested our architecture up to 9x9 kernel size on Xilinx Virtex 7 FPGA, achieving a throughput of up to 226.2 GOp/S for 3x3 kernel size.
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Authors: Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang
Date: 2021-09-07 00:00:00
Tasks: Prediction, Sequential Diagnosis

Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a patient's interactions with healthcare systems, has numerous inherent characteristics of temporality, irregularity and data insufficiency. Some recent works train healthcare predictive models by making use of sequential information in EHR data, but they are vulnerable to irregular, temporal EHR data with the states of admission/discharge from hospital, and insufficient data. To mitigate this, we propose an end-to-end robust transformer-based model called SETOR, which exploits neural ordinary differential equation to handle both irregular intervals between a patient's visits with admitted timestamps and length of stay in each visit, to alleviate the limitation of insufficient data by integrating medical ontology, and to capture the dependencies between the patient's visits by employing multi-layer transformer blocks. Experiments conducted on two real-world healthcare datasets show that, our sequential diagnoses prediction model SETOR not only achieves better predictive results than previous state-of-the-art approaches, irrespective of sufficient or insufficient training data, but also derives more interpretable embeddings of medical codes. The experimental codes are available at the GitHub repository (https://github.com/Xueping/SETOR).
Learning grounded word meaning representations on similarity graphs
Authors: Mariella Dimiccoli, Herwig Wendt, Pau Batlle
Date: 2021-09-07 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Graph Embedding

This paper introduces a novel approach to learn visually grounded meaning representations of words as low-dimensional node embeddings on an underlying graph hierarchy. The lower level of the hierarchy models modality-specific word representations through dedicated but communicating graphs, while the higher level puts these representations together on a single graph to learn a representation jointly from both modalities. The topology of each graph models similarity relations among words, and is estimated jointly with the graph embedding. The assumption underlying this model is that words sharing similar meaning correspond to communities in an underlying similarity graph in a low-dimensional space. We named this model Hierarchical Multi-Modal Similarity Graph Embedding (HM-SGE). Experimental results validate the ability of HM-SGE to simulate human similarity judgements and concept categorization, outperforming the state of the art.
Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams
Authors: Barbara Arbanas Ferreira, Tamara Petrović, Matko Orsag, J. Ramiro Martínez-de-Dios, Stjepan Bogdan
Date: 2021-09-07 00:00:00
Tasks: Scheduling

To enable safe and efficient use of multi-robot systems in everyday life, a robust and fast method for coordinating their actions must be developed. In this paper, we present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. The approach is based on representing the problem as a variant of the vehicle routing problem, and the solution is found using a distributed metaheuristic algorithm based on evolutionary computation (CBM-pop). Such an approach allows a fast and near-optimal allocation and can therefore be used for online replanning in case of task changes. Simulation results show that the approach has better computational speed and scalability without loss of optimality compared to the state-of-the-art distributed methods. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.
OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer
Authors: Hailiang Tang, Xiaoji Niu, Tisheng Zhang, You Li, Jingnan Liu
Date: 2021-09-07 00:00:00

Odometer has been proven to significantly improve the accuracy of the Global Navigation Satellite System / Inertial Navigation System (GNSS/INS) integrated vehicle navigation in GNSS-challenged environments. However, the odometer is inaccessible in many applications, especially for aftermarket devices. To apply forward speed aiding without hardware wheeled odometer, we propose OdoNet, an untethered one-dimensional Convolution Neural Network (CNN)-based pseudo-odometer model learning from a single Inertial Measurement Unit (IMU), which can act as an alternative to the wheeled odometer. Dedicated experiments have been conducted to verify the feasibility and robustness of the OdoNet. The results indicate that the IMU individuality, the vehicle loads, and the road conditions have little impact on the robustness and precision of the OdoNet, while the IMU biases and the mounting angles may notably ruin the OdoNet. Thus, a data-cleaning procedure is added to effectively mitigate the impacts of the IMU biases and the mounting angles. Compared to the process using only non-holonomic constraint (NHC), after employing the pseudo-odometer, the positioning error is reduced by around 68%, while the percentage is around 74% for the hardware wheeled odometer. In conclusion, the proposed OdoNet can be employed as an untethered pseudo-odometer for vehicle navigation, which can efficiently improve the accuracy and reliability of the positioning in GNSS-denied environments.
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition
Authors: Zhi Zhang, Sheng-hua Zhong, Yan Liu
Date: 2021-09-07 00:00:00
Tasks: Data Augmentation, EEG, Electroencephalogram (EEG), Emotion Recognition, Generative Adversarial Network, Self-Supervised Learning

The data scarcity problem in Electroencephalography (EEG) based affective computing results into difficulty in building an effective model with high accuracy and stability using machine learning algorithms especially deep learning models. Data augmentation has recently achieved considerable performance improvement for deep learning models: increased accuracy, stability, and reduced over-fitting. In this paper, we propose a novel data augmentation framework, namely Generative Adversarial Network-based Self-supervised Data Augmentation (GANSER). As the first to combine adversarial training with self-supervised learning for EEG-based emotion recognition, the proposed framework can generate high-quality and high-diversity simulated EEG samples. In particular, we utilize adversarial training to learn an EEG generator and force the generated EEG signals to approximate the distribution of real samples, ensuring the quality of augmented samples. A transformation function is employed to mask parts of EEG signals and force the generator to synthesize potential EEG signals based on the remaining parts, to produce a wide variety of samples. The masking possibility during transformation is introduced as prior knowledge to guide to extract distinguishable features for simulated EEG signals and generalize the classifier to the augmented sample space. Finally, extensive experiments demonstrate our proposed method can help emotion recognition for performance gain and achieve state-of-the-art results.
Recommendation Fairness: From Static to Dynamic
Authors: Dell Zhang, Jun Wang
Date: 2021-09-05 00:00:00
Tasks: Fairness, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding
Authors: Lele Cao, Emil Larsson, Vilhelm von Ehrenheim, Dhiana Deva Cavalcanti Rocha, Anna Martin, Sonja Horn
Date: 2021-09-07 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results. However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios. To that end, we propose a generic and end-to-end approach -- PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset. We experimentally show that PAUSE achieves, and sometimes surpasses, state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the liability of extensive manual annotation work.
Robust Predictable Control
Authors: Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine
Date: 2021-09-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Computational Efficiency, Decision Making, Reinforcement Learning (RL)

Many of the challenges facing today's reinforcement learning (RL) algorithms, such as robustness, generalization, transfer, and computational efficiency are closely related to compression. Prior work has convincingly argued why minimizing information is useful in the supervised learning setting, but standard RL algorithms lack an explicit mechanism for compression. The RL setting is unique because (1) its sequential nature allows an agent to use past information to avoid looking at future observations and (2) the agent can optimize its behavior to prefer states where decision making requires few bits. We take advantage of these properties to propose a method (RPC) for learning simple policies. This method brings together ideas from information bottlenecks, model-based RL, and bits-back coding into a simple and theoretically-justified algorithm. Our method jointly optimizes a latent-space model and policy to be self-consistent, such that the policy avoids states where the model is inaccurate. We demonstrate that our method achieves much tighter compression than prior methods, achieving up to 5x higher reward than a standard information bottleneck. We also demonstrate that our method learns policies that are more robust and generalize better to new tasks.
Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
Authors: Canwen Xu, Wangchunshu Zhou, Tao Ge, Ke Xu, Julian McAuley, Furu Wei
Date: 2021-09-07 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Knowledge Distillation, Quantization

Recent studies on compression of pretrained language models (e.g., BERT) usually use preserved accuracy as the metric for evaluation. In this paper, we propose two new metrics, label loyalty and probability loyalty that measure how closely a compressed model (i.e., student) mimics the original model (i.e., teacher). We also explore the effect of compression with regard to robustness under adversarial attacks. We benchmark quantization, pruning, knowledge distillation and progressive module replacing with loyalty and robustness. By combining multiple compression techniques, we provide a practical strategy to achieve better accuracy, loyalty and robustness.
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
Authors: Yujin Tang, David Ha
Date: 2021-09-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing the full picture. Such systems have inspired development of artificial intelligence algorithms in areas such as swarm optimization and cellular automata. Motivated by the emergence of collective behavior from complex cellular systems, we build systems that feed each sensory input from the environment into distinct, but identical neural networks, each with no fixed relationship with one another. We show that these sensory networks can be trained to integrate information received locally, and through communication via an attention mechanism, can collectively produce a globally coherent policy. Moreover, the system can still perform its task even if the ordering of its inputs is randomly permuted several times during an episode. These permutation invariant systems also display useful robustness and generalization properties that are broadly applicable. Interactive demo and videos of our results: https://attentionneuron.github.io/
Sparse Distributed Memory using Spiking Neural Networks on Nengo
Authors: Rohan Deepak Ajwani, Arshika Lalan, Basabdatta Sen Bhattacharya, Joy Bose
Date: 2021-09-07 00:00:00

We present a Spiking Neural Network (SNN) based Sparse Distributed Memory (SDM) implemented on the Nengo framework. We have based our work on previous work by Furber et al, 2004, implementing SDM using N-of-M codes. As an integral part of the SDM design, we have implemented Correlation Matrix Memory (CMM) using SNN on Nengo. Our SNN implementation uses Leaky Integrate and Fire (LIF) spiking neuron models on Nengo. Our objective is to understand how well SNN-based SDMs perform in comparison to conventional SDMs. Towards this, we have simulated both conventional and SNN-based SDM and CMM on Nengo. We observe that SNN-based models perform similarly as the conventional ones. In order to evaluate the performance of different SNNs, we repeated the experiment using Adaptive-LIF, Spiking Rectified Linear Unit, and Izhikevich models and obtained similar results. We conclude that it is indeed feasible to develop some types of associative memories using spiking neurons whose memory capacity and other features are similar to the performance without SNNs. Finally we have implemented an application where MNIST images, encoded with N-of-M codes, are associated with their labels and stored in the SNN-based SDM.
SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"
Authors: Zhixue Zhao, Ziqi Zhang, Frank Hopfgartner
Date: 2021-09-06 00:00:00
Tasks: Toxic Comment Classification

Toxic comment classification models are often found biased toward identity terms which are terms characterizing a specific group of people such as "Muslim" and "black". Such bias is commonly reflected in false-positive predictions, i.e. non-toxic comments with identity terms. In this work, we propose a novel approach to tackle such bias in toxic comment classification, leveraging the notion of subjectivity level of a comment and the presence of identity terms. We hypothesize that when a comment is made about a group of people that is characterized by an identity term, the likelihood of that comment being toxic is associated with the subjectivity level of the comment, i.e. the extent to which the comment conveys personal feelings and opinions. Building upon the BERT model, we propose a new structure that is able to leverage these features, and thoroughly evaluate our model on 4 datasets of varying sizes and representing different social media platforms. The results show that our model can consistently outperform BERT and a SOTA model devised to address identity term bias in a different way, with a maximum improvement in F1 of 2.43% and 1.91% respectively.
Text-to-Table: A New Way of Information Extraction
Authors: Xueqing Wu, Jiacheng Zhang, Hang Li
Date: 2021-09-06 00:00:00
Proceeding: ACL 2022 5
Tasks: Language Modelling, Relation Extraction

We study a new problem setting of information extraction (IE), referred to as text-to-table. In text-to-table, given a text, one creates a table or several tables expressing the main content of the text, while the model is learned from text-table pair data. The problem setting differs from those of the existing methods for IE. First, the extraction can be carried out from long texts to large tables with complex structures. Second, the extraction is entirely data-driven, and there is no need to explicitly define the schemas. As far as we know, there has been no previous work that studies the problem. In this work, we formalize text-to-table as a sequence-to-sequence (seq2seq) problem. We first employ a seq2seq model fine-tuned from a pre-trained language model to perform the task. We also develop a new method within the seq2seq approach, exploiting two additional techniques in table generation: table constraint and table relation embeddings. We consider text-to-table as an inverse problem of the well-studied table-to-text, and make use of four existing table-to-text datasets in our experiments on text-to-table. Experimental results show that the vanilla seq2seq model can outperform the baseline methods of using relation extraction and named entity extraction. The results also show that our method can further boost the performances of the vanilla seq2seq model. We further discuss the main challenges of the proposed task. The code and data are available at https://github.com/shirley-wu/text_to_table.