Reviewerly/exHarmony huggingface dataset:
BSQA: Bidirectional Stacked Question Answering Architecture for End-to-end Event Extraction

Event extraction (EE) is a fundamental task of natural language processing, which aims to recognize the occurrence of events and their arguments in the texts. Most previous works on EE employ the pipeli <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ne</sup> framework to firstly <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex</sup> tract event triggers, then to extract arguments for the given event triggers. This framework is very simple, but error cascades are inevitable. Besides, in some cases, the extraction of arguments is easier than event triggers. Furthermore, arguments can help in event trigger extraction. In order to reduce error cascades and take advantage of the complementarity between event triggers and arguments, we propose a bidirectional stacked question answering (BSQA) framework for event extraction. Specifically, we first unify event trigger extraction and argument extraction into an independent span detection and classification component. Secondly, for each component, we devise a non-restrictive extraction query, a restrictive extraction query and a set of restrictive classification queries to accomplish the corresponding span detection and classification task. Finally, we stack the two components for event trigger and argument extraction bidirectionally. One direction sequentially extract event triggers and arguments, while the other direction recognizes arguments first, then event triggers. Experimental results on the FewFC corpus show the effectiveness of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">our</sup> proposed approach.
Coreference-aware Double-channel Attention Network for Multi-party Dialogue Reading Comprehension

We tackle Multi-party Dialogue Reading Comprehension (abbr., MDRC). MDRC stands for an extractive reading comprehension task grounded on a batch of dialogues among multiple interlocutors. It is challenging due to the requirement of understanding cross-utterance contexts and relationships in a multi-turn multi-party conversation. Previous studies have made great efforts on the utterance profiling of a single interlocutor and graph-based interaction modeling. The corresponding solutions contribute to the answer-oriented reasoning on a series of well-organized and thread-aware conversational contexts. However, the current MDRC models still suffer from two bottlenecks. On the one hand, a pronoun like “it” most probably produces multi-skip reasoning throughout the utterances of different interlocutors. On the other hand, an MDRC encoder is potentially puzzled by fuzzy features, i.e., the mixture of inner linguistic features in utterances and external interactive features among utterances. To overcome the bottlenecks, we propose a coreference-aware attention modeling method to strengthen the reasoning ability. In addition, we construct a two-channel encoding network. It separately encodes utterance profiles and interactive relationships, so as to relieve the confusion among heterogeneous features. We experiment on the benchmark corpora Molweni and FriendsQA. Experimental results demonstrate that our approach yields substantial improvements on both corpora, compared to the fine-tuned BERT and ELECTRA baselines. The maximum performance gain is about 2.5% <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F\mathbf{1}-\mathbf{score}$</tex> . Besides, our MDRC models outperform the state-of-the-art in most cases.
Community-Aware Federated Video Summarization

Video summarization aims to extract representative frames to retain high-level information. Increasing concerns about privacy issues have been raised because conventional large-scale training requires users to upload video samples that may inevitably release sensitive information. In this paper, we thoroughly discuss the Federated Video Summarization problem, i.e., how to obtain a robust video summarization model when video data is distributed on private data islands. Our key contribution includes 1) We propose a fundamental Frame-Based aggregation method to video-related tasks, which differs from the sample-based aggregation in conventional FedAvg. 2) To mitigate the heterogeneous distribution due to community diversity, we propose the Community-Aware Clustering Federated Video Summarization Framework (CFed-VS) that clusters clients via a novel data-driven clustering approach. 3) We further tackle the challenging non-IID setting with a proposed Mixture Transformer, which manifests state-of-the-art performance via extensive quantitative and qualitative experiments on TVSum and SumMe datasets.
Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency

A major challenge in deep reinforcement learning is that it requires more data to converge to an policy for complex problems. One way to improve sample efficiency is to use n-step updates to reduce the number of samples required to converge to a good policy. However n-step updates are known to be brittle and difficult to tune. Elastic Step DQN has shown that it is possible to automate the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> in DQN to solve problems involving discrete action spaces, however the efficacy of the technique when applied on more complex problems and against problems with continuous action spaces is yet to be shown. In this paper we adapt the innovations proposed by Elastic Step DQN onto the DDPG algorithm and show empirically that Elastic Step DDPG is able to achieve a much stronger final training policy and is more sample efficient than DDPG.
CL-CSP: Contrastive Learning with Continuous Semantic Perturbations for Neural Dialogue Generation

The principal task in neural dialogue generation is to learn to generate coherent and diverse responses conditioned on dialogue history, and thus imitate the intrinsic conversational skills of real humans. Conventional training approaches for dialogue generation (e.g., Maximum Likely Estimation (MLE)) focus on capturing coherent syntactic patterns, but fall short of showcasing diversity in expression. In this paper, we utilize contrastive learning for dialogue, which extends the conventional MLE-based dialogue training with a contrastive counterpart, where dialogue generation models can be explicitly trained on multiple positive and negative pairs. However, naive contrastive learning with discrete manipulations is sub-optimal, since they tend to sample examples that can be easily distinguished. To migrate this problem, we propose a novel contrastive learning paradigm with continuous semantic perturbations (CL-CSP), which constructs positive examples within an adjacency semantic region and pushes those negative examples far away from the semantic region. Such “soft” positive and negative pairs constructed by CL-CSP guides the model to distinguish positive instances from negative ones better. We conduct extensive experiments to demonstrate that our proposed method improves the diversity of response generation on two benchmarks, showing that our proposed techniques are crucial to achieving superior performance.
FastAct: A Lightweight Actor Compression Framework for Fast Policy Learning

Deep reinforcement learning outperforms humans in a variety of complex decision-making tasks. However, training an effective policy requires millions of interactions between the agent and the target environment, making the learning process time consuming and resource intensive. Inspired by neural network compression, we propose a lightweight policy learning framework, FastAct, to accelerate policy training by speeding up Actor inference. Specifically, FastAct accelerates the Actor by dynamically using multiple compression techniques to quickly generate experience data for policy training. Compared to the traditional framework, the FastAct adds two modules, Compressor and Scheduler. The Compressor integrates multiple compression algorithms through the chain-of-responsibility design pattern and aims to maximize the inference speed of the Actors. To ensure the quality of the experience data, we develop the Scheduler to dynamically adjust the compression algorithm according to the statistical information of the Actor. In addition, FastAct applies the off-policy value estimator V-trace to correct the value function of the target policy. We implemented FastAct based on IMPALA and conducted extensive experiments on Atari. Compared with IMPALA, FastAct speeds up the Actor by 2.0x to 4.1x. And FastAct reduces training time by 29.8% to 40.3% while maintaining a similar result.
Mutually Promoted Hierarchical Learning for Incremental Implicitly-Refined Classification

Class incremental learning devotes to learning a classification model from incrementally arriving training data. Existing methods tend to use a single-headed layout due to the lack of task delimiter while testing. However, this is not suitable for Incremental Implicitly-Refined Classification (IIRC), an extension to class incremental learning where different classes could have two granularity levels. In IIRC, each sample could have a coarse label and a fine label. Without considering hierarchical relations among classes, it's difficult to distinguish a subclass from its siblings and assign them to the same superclass while alleviating catastrophic forgetting. In this paper, we propose a new framework called Mutually Promoted Hierarchical Learning (MPHL) to solve IIRC. MPHL learns separate latent spaces for superclasses and subclasses to coordinate the representations of two granularities and utilizes the hierarchy to facilitate the training process of each other. While using knowledge distillation, subclasses are treated as replay samples of their parents, and coarse labels are used to eliminate interference from non-siblings. To reduce the risk of selecting unwarranted fine-grained labels, we further propose a self-adaptive threshold strategy to detect out-of-distribution samples. Extensive experiments on IIRC-CIFAR100 and IIRC-ImageNet show that MPHL achieves state-of-the-art results.
The Effect of System Timescale on Virtual Node Connectivity within Delay-Feedback Reservoirs

The delay-feedback reservoir is a branch of reservoir computing that has allowed for a more hardware friendly implementation by reducing the typically large number of inputs and outputs within neural networks to a single physical input and output. This is achieved by using a single non-linear node and time-multiplexing the input signal with a masking signal, to create many “virtual neurons” within a reservoir; emulating a larger spatial neural network. While the relationship of the number of virtual nodes, masking frequency, and overall delay length is well known, the effect that the timescale has on computational performance is not widely investigated or understood; usually because working with specific substrates implies the reservoir is confined to its inherent timescales. Hence, there is currently no general methodology for tuning delay-feedback reservoir systems to specific applications. Here, we create a parameterisable hardware-realistic computational model in order to simulate a delay-feedback reservoir operating at different timescales, and explore the effect this has on the connectivity between the virtual nodes and the computational performance of the system. Our results show that the timescale has indeed a significant effect on computational tasks with a long-term dependency on previous input stimuli. We then show that it is possible to emulate larger virtual node networks within smaller ones with little loss in performance and an increase in efficiency. This is an essential step towards understanding the function of the timescale within a delay-feedback reservoir, and a methodology to enable systematic tailoring of delay-feedback reservoirs to specific computational tasks across different timescales.
Deep Hashing Capable of Adding New Dataset without Class Labels

Deep hashing has realized very accurate similar image retrieval by learning the hash function via deep neural networks (DNN). Most deep hashing methods assume that queries originate from the same image dataset as the training images. However in practice, as the search system becomes older, it will encounter more query images that are not covered by the learned image dataset, To cope with this issue, we propose a solution named UIDH (Unsupervised Incremental Deep Hashing) that enables a deep hashing trained on one image dataset to learn an additional image dataset as new training data. First, UIDH obtains the initial hash function that outputs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -bit hash codes by learning the first image dataset with supervision. Later, UIDH learns the additional image dataset with unsupervised incremental learning and derives the second hash function that outputs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\frac{K}{2}$</tex> -bit hash codes. The final hash function in UIDH concatenates the upper half of the initial function with the second hash function. It maintains the high retrieval accuracy for the first dataset thanks to the initial supervised hashing and improves the similarity search for the additional dataset supported by the second unsupervised hashing. In this way, UIDH realizes an image search system that can treat the two dataset simultaneously. The novelty in UIDH is to handle the additional dataset without class labels.
Handling Class Imbalance by Estimating Minority Class Statistics

The problem of class imbalance arises in machine learning due to the unequal class-specific distribution of data, where most samples belong to one class, and only a few represent the others. To tackle this issue, one paradigm is to use oversampling techniques that synthesize artificial samples of the minority class using the convex combination of the minority class samples taken in some specialized way for different methods. Existing methods do not take into account any information regarding the actual distribution of the minority class, which leads to inconsistencies between the generated distribution and the actual distribution that the minority class might have. In this paper, we propose a parametrization-based method that tries to estimate the statistics of the minority class samples using the statistics of the nearby classes. Using the different hyperparameters, we can control the distribution such that it may approximate the original distribution. Experiments using synthetic and real-world benchmark datasets demonstrate the usefulness of our techniques across multiple metrics.
KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answering

Generative commonsense question answering (GenCQA) is a task of automatically generating a list of answers given a question. The answer list is required to cover all reasonable answers. This presents the considerable challenges of producing diverse answers and ranking them properly. Incorporating a variety of closely-related background knowledge into the encoding of questions enables the generation of different answers. Meanwhile, learning to distinguish positive answers from negative ones potentially enhances the probabilistic estimation of plausibility, and accordingly, the plausibility-based ranking. Therefore, we propose a Knowledge Enhancement and Plausibility Ranking (KEPR) approach grounded on the Generate-Then-Rank pipeline architecture. Specifically, we expand questions in terms of Wiktionary commonsense knowledge of keywords, and reformulate them with normalized patterns. Dense passage retrieval is utilized for capturing relevant knowledge, and different PLM-based (BART, GPT2 and T5) networks are used for generating answers. On the other hand, we develop an ELECTRA-based answer ranking model, where logistic regression is conducted during training, with the aim of approximating different levels of plausibility in a polar classification scenario. Extensive experiments on the benchmark ProtoQA show that KEPR obtains substantial improvements, compared to the strong baselines. Within the experimental models, the T5-based GenCQA with KEPR obtains the best performance, which is up to 60.91% at the primary canonical metric Inc@3. It outperforms the existing GenCQA models on the current leaderboard of ProtoQA.
EBAMQ: Exposure Bias Alleviation via Moment Matching for Question Generation

Question Generation (QG) aims to automatically raise questions given contexts. Due to the discrepancy between the training (supervised learning) and inference (autoregressive) modes, the current QG task suffers from exposure bias, i.e., the phenomenon of error propagation induced by mistakenly-generated tokens in the preceding subsequence. To address this issue, we propose to utilize moment matching to progressively alleviate exposure biases, which is referred to EBAMQ for short in this paper. EBAMQ enhances QG by imposing positive mitigation effects on the distributed representation. This is implemented by two components, including (1) adaptive moment matching and (2) assistant-cooperating data augmentation. Within the two components, adaptive moment matching plays the dominant role. It enables the sharing of feature space among different samples that derive from binary channels (i.e., training and inference). More importantly, it provides more similar representations for synonymous or homologous tokens. In addition, we introduce the pseudo-masked technique to generate additional reference questions. It contributes to the reduction of inevitable constraints of ground-truth instances, and therefore, it helps to improve the robustness. Extensive experiments are conducted on the benchmark dataset SQuAD. Experimental results show that EBAMD produces substantial improvements for all the metrics (BLEU-4, ROUGE-L and METEOR) compared to the UniLM-based baseline, and it outperforms the state-of-the-art QG models.
A Scalable GPT-2 Inference Hardware Architecture on FPGA

Transformer-based architectures using attention mechanisms are a class of learning architectures for sequence processing tasks. These include architectures such as the generative pretrained transformer (GPT) and the bidirectional encoder representations from transformers (BERT). GPT-2 is a popular sequence learning architecture that uses transformer architecture. GPT-2 is trained on text prediction, and the network parameters obtained during this training process can be used in various other tasks like text classification and premise-hypothesis testing. Edge computing is an recent trend in which training is done on cloud or server with multiple GPUs, but inference is done on edge devices like mobile phones to reduce latency and improve privacy. This necessitates a study of GPT-2 performance and complexity to distill hardware-based architectures for their usability on edge devices. In this paper, a single layer of GPT-2 based inference architecture is implemented on Virtex-7 xc7vx485tffg1761-2 FPGA board. The inference engine has model dimensionality of 128 and latency of 1.637 ms while operating at 142.44 MHz, consuming 85.6K flip-flops and 96.8K lookup tables, achieving 1.73x speedup compared to previously reported work on transformer-based architecture. The approach proposed in this paper is scalable to models of higher dimensionality.
An Anchor-Semantics-Aware Neural Preference Propagation Model for Session-based Recommendation

Session-based recommendation aims to predict user's next action based on the anonymous sessions. Recent studies mainly apply Graph Neural Networks (GNN) to model the complex item-transitions and transfer the collaborative signals among sessions. However, most existing methods ignore the semantic inconsistency problem cause by item functional diversity and popular item during the process of cross-session collaborative signal capturing, which may lead to a preference negative transfer between irrelevant sessions. In addition, current works neglect to retain the semantic discriminability when learning the representations of sessions and items, and consequently degenerate the personalized recommendation. In this paper, we propose a novel model, called Anchor-Semantics-Aware Neural Preference Propagation (ASA-NPP), to simulate the transfer of collaborative signals with an anchor-semantics-aware recursive neural propagation over a designed graph. Specifically, we devise a Session Graph with Anchor Links (SGAL) based on all sessions, and then present an Anchor-Semantics Attention Network (ASAN) to transfer the semantically consistent cross-session collaborative signals and learn session-specific embeddings encoding different session semantics. Furthermore, we propose a Session Semantics Enhancement (SSE) module to improve the semantic discriminability of the learned representation via an elaborate self-supervised learning task. Extensive experiments on real-world datasets demonstrate the effectiveness and superiority of ASA-NPP over the state-of-the-art methods.
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge

Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain, where it is quite common to deal with large knowledge graphs underlying complex interactions between biological and chemical objects. Recently in the literature, the PharmKG dataset has been proposed as one of the most challenging knowledge graph biomedical benchmark, with hundreds of thousands of relational facts between genes, diseases and chemicals. Despite KGEs can scale to very large relational domains, they generally fail at representing more complex relational dependencies between facts, like logic rules, which may be fundamental in complex experimental settings. In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset. To this end, we adopt Relational Reasoning Network (R2N), a recently proposed neural-symbolic approach showing promising results on knowledge graph completion tasks. An R2N uses the available logic rules to build a neural architecture that reasons over KGE latent representations. In the experiments, we show that our approach is able to significantly improve the current state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study to experimentally compare the effect of alternative sets of rules according to different selection criteria and varying the number of considered rules.