Reviewerly/exHarmony huggingface dataset:
Aspect-Controlled Neural Argument Generation

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn.However, finding and formulating arguments can be challenging.In this work, we present the Arg-CTRL-a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect.We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects.Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspectspecific arguments, applicable to automatic counter-argument generation.We publish the model weights and all datasets and code to train the Arg-CTRL. 1Nuclear reactors produce radioactive waste ... Nuclear Energy CONIt not only guarantees base load power ...
AMR Parsing with Action-Pointer Transformer

Meaning Representation parsing is a sentence-to-graph prediction task where target nodes are not explicitly aligned to sentence tokens.However, since graph nodes are semantically based on one or more sentence tokens, implicit alignments can be derived.Transition-based parsers operate over the sentence from left to right, capturing this inductive bias via alignments at the cost of limited expressiveness.In this work, we propose a transition-based system that combines hard-attention over sentences with a targetside action pointer mechanism to decouple source tokens from node representations and address alignments.We model the transitions as well as the pointer mechanism through straightforward modifications within a single Transformer architecture.Parser state and graph structure information are efficiently encoded using attention heads.We show that our action-pointer approach leads to increased expressiveness and attains large gains (+1.6 points) against the best transition-based AMR parser in very similar conditions.While using no graph re-categorization, our single model yields the second best SMATCH score on AMR 2.0 (81.8), which is further improved to 83.4 with silver data and ensemble decoding.
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment

Non-autoregressive encoder-decoder models greatly improve decoding speed over autoregressive models, at the expense of generation quality.To mitigate this, iterative decoding models repeatedly infill or refine the proposal of a non-autoregressive model.However, editing at the level of output sequences limits model flexibility.We instead propose iterative realignment, which by refining latent alignments allows more flexible edits in fewer steps.Our model, Align-Refine, is an end-to-end Transformer which iteratively realigns connectionist temporal classification (CTC) alignments.On the WSJ dataset, Align-Refine matches an autoregressive baseline with a 14× decoding speedup; on LibriSpeech, we reach an LM-free testother WER of 9.0% (19% relative improvement on comparable work) in three iterations.We release our code at https://github.com/
Focused Attention Improves Document-Grounded Generation

Document grounded generation is the task of using the information provided in a document to improve text generation.This work focuses on two different document grounded generation tasks: Wikipedia Update Generation task and Dialogue response generation.Our work introduces two novel adaptations of large scale pre-trained encoder-decoder models focusing on building context driven representation of the document and enabling specific attention to the information in the document.Additionally, we provide a stronger BART baseline for these tasks.Our proposed techniques outperform existing methods on both automated (at least 48% increase in BLEU-4 points) and human evaluation for closeness to reference and relevance to the document.Furthermore, we perform comprehensive manual inspection of the generated output and categorize errors to provide insights into future directions in modeling these tasks.
Detoxifying Language Models Risks Marginalizing Minority Voices

Language models (LMs) must be both safe and equitable to be responsibly deployed in practice.With safety in mind, numerous detoxification techniques (e.g., Dathathri et al. 2020;Krause et al. 2020) have been proposed to mitigate toxic LM generations.In this work, we show that these detoxification techniques hurt equity: they decrease the utility of LMs on language used by marginalized groups (e.g., African-American English and minority identity mentions).In particular, we perform automatic and human evaluations of text generation quality when LMs are conditioned on inputs with different dialects and group identifiers.We find that detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups.We identify that these failures stem from detoxification methods exploiting spurious correlations in toxicity datasets.Overall, our results highlight the tension between the controllability and distributional robustness of LMs.
SSD-GAN: Measuring the Realness in the Spatial and Spectral Domains

This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network architecture. This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy between generated images and real images. Since the Fourier transform is a bijective mapping, we argue that reducing this spectrum discrepancy would boost the performance of GANs. To this end, we introduce SSD-GAN, an enhancement of GANs to alleviate the spectral information loss in the discriminator. Specifically, we propose to embed a frequency-aware classifier into the discriminator to measure the realness of the input in both the spatial and spectral domains. With the enhanced discriminator, the generator of SSD-GAN is encouraged to learn high-frequency content of real data and generate exact details. The proposed method is general and can be easily integrated into most existing GANs framework without excessive cost. The effectiveness of SSD-GAN is validated on various network architectures, objective functions, and datasets. Code is available at https://github.com/cyq373/SSD-GAN.
SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition

Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Rectification (i.e., spatial transformers) as the preprocessing stage is one popular approach and extensively studied. However, chromatic difficulties in complex scenes have not been paid much attention on. In this work, we introduce a new learnable geometric-unrelated rectification, Structure-Preserving Inner Offset Network (SPIN), which allows the color manipulation of source data within the network. This differentiable module can be inserted before any recognition architecture to ease the downstream tasks, giving neural networks the ability to actively transform input intensity rather than only the spatial rectification. It can also serve as a complementary module to known spatial transformations and work in both independent and collaborative ways with them. Extensive experiments show the proposed transformation outperforms existing rectification networks and has comparable performance among the state-of-the-arts.
Automated Storytelling via Causal, Commonsense Plot Ordering

Automated story plot generation is the task of generating a coherent sequence of plot events. Causal relations between plot events are believed to increase the perception of story and plot coherence. In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning. We demonstrate C2PO, an approach to narrative generation that operationalizes this concept through Causal, Commonsense Plot Ordering. Using human-participant protocols, we evaluate our system against baseline systems with different commonsense reasoning reasoning and inductive biases to determine the role of soft causal relations in perceived story quality. Through these studies we also probe the interplay of how changes in commonsense norms across storytelling genres affect perceptions of story quality.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence

Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation.Despite the success in modeling intra-sentence coherence, existing generation models (e.g., BART) still struggle to maintain a coherent event sequence throughout the generated text.We conjecture that this is because of the difficulty for the decoder to capture the high-level semantics and discourse structures in the context beyond token-level co-occurrence.In this paper, we propose a long text generation model, which can represent the prefix sentences at sentence level and discourse level in the decoding process.To this end, we propose two pretraining objectives to learn the representations by predicting inter-sentence semantic similarity and distinguishing between normal and shuffled sentence orders.Extensive experiments show that our model can generate more coherent texts than state-of-the-art baselines.
Quantum-inspired Neural Network for Conversational Emotion Recognition

We provide a novel perspective on conversational emotion recognition by drawing an analogy between the task and a complete span of quantum measurement. We characterize different steps of quantum measurement in the process of recognizing speakers' emotions in conversation, and stitch them up with a quantum-like neural network. The quantum-like layers are implemented by complex-valued operations to ensure an authentic adoption of quantum concepts, which naturally enables conversational context modeling and multimodal fusion. We borrow an existing algorithm to learn the complex-valued network weights, so that the quantum-like procedure is conducted in a data-driven manner. Our model is comparable to state-of-the-art approaches on two benchmarking datasets, and provide a quantum view to understand conversational emotion recognition.
Don’t Miss the Labels: Label-semantic Augmented Meta-Learner for Few-Shot Text Classification

Increasing studies leverage pre-trained language models and meta-learning frameworks to solve few-shot text classification problems.Most of the current studies focus on building a meta-learner from the information of input texts but ignore abundant semantic information beneath class labels.In this work, we show that class-label information can be utilized for extracting more discriminative feature representation of the input text from a pretrained language model like BERT, and can achieve a performance boost when the samples are scarce.Building on top of this discovery, we propose a framework called Labelsemantic augmented meta-learner (LaSAML) to make full use of label semantics.We systematically investigate various factors in this framework and show that it can be plugged into the existing few-shot text classification system.Through extensive experiments, we demonstrate that the few-shot text classification system upgraded by LaSAML can lead to significant performance improvement over its original counterparts.
Re-TACRED: Addressing Shortcomings of the TACRED Dataset

TACRED is one of the largest and most widely used sentence-level relation extraction datasets. Proposed models that are evaluated using this dataset consistently set new state-of-the-art performance. However, they still exhibit large error rates despite leveraging external knowledge and unsupervised pretraining on large text corpora. A recent study suggested that this may be due to poor dataset quality. The study observed that over 50% of the most challenging sentences from the development and test sets are incorrectly labeled and account for an average drop of 8% f1-score in model performance. However, this study was limited to a small biased sample of 5k (out of a total of 106k) sentences, substantially restricting the generalizability and broader implications of its findings. In this paper, we address these shortcomings by: (i) performing a comprehensive study over the whole TACRED dataset, (ii) proposing an improved crowdsourcing strategy and deploying it to re-annotate the whole dataset, and (iii) performing a thorough analysis to understand how correcting the TACRED annotations affects previously published results. After verification, we observed that 23.9% of TACRED labels are incorrect. Moreover, evaluating several models on our revised dataset yields an average f1-score improvement of 14.3% and helps uncover significant relationships between the different models (rather than simply offsetting or scaling their scores by a constant factor). Finally, aside from our analysis we also release Re-TACRED, a new completely re-annotated version of the TACRED dataset that can be used to perform reliable evaluation of relation extraction models.
Ecco: An Open Source Library for the Explainability of Transformer Language Models

J Alammar. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. 2021.
Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies

Monitoring treatment response in longitudinal studies plays an important role in clinical practice. Accurately identifying lesions across serial imaging follow-up is the core to the monitoring procedure. Typically this incorporates both image and anatomical considerations. However, matching lesions manually is labor-intensive and time-consuming. In this work, we present deep lesion tracker (DLT), a deep learning approach that uses both appearance- and anatomical-based signals. To incorporate anatomical constraints, we propose an anatomical signal encoder, which prevents lesions being matched with visually similar but spurious regions. In addition, we present a new formulation for Siamese networks that avoids the heavy computational loads of 3D cross-correlation. To present our network with greater varieties of images, we also propose a self-supervised learning (SSL) strategy to train trackers with unpaired images, overcoming barriers to data collection. To train and evaluate our tracker, we introduce and release the first lesion tracking benchmark, consisting of 3891 lesion pairs from the public DeepLesion database. The proposed method, DLT, locates lesion centers with a mean error distance of 7mm. This is 5% better than a leading registration algorithm while running 14 times faster on whole CT volumes. We demonstrate even greater improvements over detector or similarity-learning alternatives. DLT also generalizes well on an external clinical test set of 100 longitudinal studies, achieving 88% accuracy. Finally, we plug DLT into an automatic tumor monitoring workflow where it leads to an accuracy of 85% in assessing lesion treatment responses, which is only 0.46% lower than the accuracy of manual inputs.
TransTailor: Pruning the Pre-trained Model for Improved Transfer Learning

The increasing of pre-trained models has significantly facilitated the performance on limited data tasks with transfer learning. However, progress on transfer learning mainly focuses on optimizing the weights of pre-trained models, which ignores the structure mismatch between the model and the target task. This paper aims to improve the transfer performance from another angle - in addition to tuning the weights, we tune the structure of pre-trained models, in order to better match the target task. To this end, we propose TransTailor, targeting at pruning the pre-trained model for improved transfer learning. Different from traditional pruning pipelines, we prune and fine-tune the pre-trained model according to the target-aware weight importance, generating an optimal sub-model tailored for a specific target task. In this way, we transfer a more suitable sub-structure that can be applied during fine-tuning to benefit the final performance. Extensive experiments on multiple pre-trained models and datasets demonstrate that TransTailor outperforms the traditional pruning methods and achieves competitive or even better performance than other state-of-the-art transfer learning methods while using a smaller model. Notably, on the Stanford Dogs dataset, TransTailor can achieve 2.7% accuracy improvement over other transfer methods with 20% fewer FLOPs.
Transferable Dialogue Systems and User Simulators

Bo-Hsiang Tseng, Yinpei Dai, Florian Kreyssig, Bill Byrne. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
A Novel Neural Network Model for Traffic Sign Detection and Recognition under Extreme Conditions

Traffic sign detection is extremely important in autonomous driving and transportation safety systems. However, the accurate detection of traffic signs remains challenging, especially under extreme conditions. This paper proposes a novel model called Traffic Sign Yolo (TS-Yolo) based on the convolutional neural network to improve the detection and recognition accuracy of traffic signs, especially under low visibility and extremely restricted vision conditions. A copy-and-paste data augmentation method was used to build a large number of new samples based on existing traffic-sign datasets. Based on You Only Look Once (YoloV5), the mixed depth-wise convolution (MixConv) was employed to mix different kernel sizes in a single convolution operation, so that different patterns with various resolutions can be captured. Furthermore, the attentional feature fusion (AFF) module was integrated to fuse the features based on attention from same-layer to cross-layer scenarios, including short and long skip connections, and even performing the initial fusion with itself. The experimental results demonstrated that, using the YoloV5 dataset with augmentation, the precision was 71.92, which was increased by 34.56 compared with the data without augmentation, and the mean average precision mAP_0.5 was 80.05, which was increased by 33.11 compared with the data without augmentation. When MixConv and AFF were applied to the TS-Yolo model, the precision was 74.53 and 2.61 higher than that with data augmentation only, and the value of mAP_0.5 was 83.73 and 3.68 higher than that based on the YoloV5 dataset with augmentation only. Overall, the performance of the proposed method was competitive with the latest traffic sign detection approaches.
3D Interactive Learning Environment as a Tool for Knowledge Transfer and Retention

The article deals with the design of virtual reality (VR) interactive training as a teaching method and its effect on knowledge transfer and retention of students. The first part presents the methodology of the VR interactive training design. The second part utilizes the created interactive training for a case study to evaluate its effect on the teaching process and to examine the potential of VR interactive training as a sustainable teaching method. The study took place at the Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Žilina. Volunteers were divided into two groups. The first group used VR interactive training as a teaching method, while the second group used the conventional method. Both groups then underwent tests. The main goal was to evaluate the effect of the VR interactive training on the teaching process in comparison to the conventional method while trying to identify the key elements of the VR interactive training design and its influence on knowledge transfer and retention in a sustainable learning environment. At the start of the case study, four hypotheses were formed, questioning the effect of interactive training on knowledge transfer and retention in the long and short term, and its overall influence on the teaching process. Obtained data were then used to evaluate these hypotheses.
SWAGAN

In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs). Even so, these networks still suffer from degradation in quality for high-frequency content, stemming from a spectrally biased architecture, and similarly unfavorable loss functions. To address this issue, we present a novel general-purpose Style and WAvelet based GAN (SWAGAN) that implements progressive generation in the frequency domain. SWAGAN incorporates wavelets throughout its generator and discriminator architectures, enforcing a frequency-aware latent representation at every step of the way. This approach, designed to directly tackle the spectral bias of neural networks, yields an improvement in the ability to generate medium and high frequency content, including structures which other networks fail to learn. We demonstrate the advantage of our method by integrating it into the SyleGAN2 framework, and verifying that content generation in the wavelet domain leads to more realistic high-frequency content, even when trained for fewer iterations. Furthermore, we verify that our model's latent space retains the qualities that allow StyleGAN to serve as a basis for a multitude of editing tasks, and show that our frequency-aware approach also induces improved high-frequency performance in downstream tasks.
Quantum imaginary-time evolution algorithm for quantum field theories with continuous variables

We calculate the energy levels and corresponding eigenstates of an interacting scalar quantum field theory on a lattice using a continuous-variable version of the quantum imaginary-time evolution algorithm. Only a single qumode is needed for the simulation of the field at each point on the lattice. Our quantum algorithm avoids the use of non-Gaussian quantum gates and relies, instead, on detectors projecting onto eigenstates of the photon-number operator. Using Xanadu's Strawberry Fields photonic simulator, we obtain results on energy levels that are in very good agreement with results from exact calculations. We propose an experimental setup that can be realized with existing technology.
A Bayesian analysis of classical shadows

The method of classical shadows heralds unprecedented opportunities for quantum estimation with limited measurements [H.-Y. Huang, R. Kueng, and J. Preskill, Nat. Phys. 16, 1050 (2020)]. Yet its relationship to established quantum tomographic approaches, particularly those based on likelihood models, remains unclear. In this article, we investigate classical shadows through the lens of Bayesian mean estimation (BME). In direct tests on numerical data, BME is found to attain significantly lower error on average, but classical shadows prove remarkably more accurate in specific situations -- such as high-fidelity ground truth states -- which are improbable in a fully uniform Hilbert space. We then introduce an observable-oriented pseudo-likelihood that successfully emulates the dimension-independence and state-specific optimality of classical shadows, but within a Bayesian framework that ensures only physical states. Our research reveals how classical shadows effect important departures from conventional thinking in quantum state estimation, as well as the utility of Bayesian methods for uncovering and formalizing statistical assumptions.