Reviewerly/exHarmony huggingface dataset:
Segmentation of Tweets with URLs and its Applications to Sentiment Analysis

An important means for disseminating information in social media platforms is by including URLs that point to external sources in user posts. In Twitter, we estimate that about 21% of the daily stream of English-language tweets contain URLs. We notice that NLP tools make little attempt at understanding the relationship between the content of the URL and the text surrounding it in a tweet. In this work, we study the structure of tweets with URLs relative to the content of the Web documents pointed to by the URLs. We identify several segments classes that may appear in a tweet with URLs, such as the title of a Web page and the user's original content. Our goals in this paper are: introduce, define, and analyze the segmentation problem of tweets with URLs, develop an effective algorithm to solve it, and show that our solution can benefit sentiment analysis on Twitter. We also show that the problem is an instance of the block edit distance problem, and thus an NP-hard problem.
Learning to Generate Task-Specific Adapters from Task Description

Pre-trained text-to-text transformers such as BART have achieved impressive performance across a range of NLP tasks.Recent study further shows that they can learn to generalize to novel tasks, by including task descriptions as part of the source sequence and training the model with (source, target) examples.At test time, these fine-tuned models can make inferences on new tasks using the new task descriptions as part of the input.However, this approach has potential limitations, as the model learns to solve individual (source, target) examples (i.e., at the instance level), instead of learning to solve tasks by taking all examples within a task as a whole (i.e., at the task level).To this end, we introduce HYPTER, a framework that improves text-to-text transformer's generalization ability to unseen tasks by training a hypernetwork to generate task-specific, light-weight adapters from task descriptions.Experiments on ZEST dataset and a synthetic SQuAD dataset demonstrate that HYPTER improves upon fine-tuning baselines.Notably, when using BART-Large as the main network, HYPTER brings 11.3% comparative improvement on ZEST dataset. 1
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues

Interpersonal language style shifting in dialogues is an interesting and almost instinctive ability of human. Understanding interpersonal relationship from language content is also a crucial step toward further understanding dialogues. Previous work mainly focuses on relation extraction between named entities in texts or within a single dialogue session. In this paper, we propose the task of relation classification of interlocutors based on their dialogues. We crawled movie scripts from IMSDb, and annotated the relation label for each session according to 13 pre-defined relationships. The annotated dataset DDRel consists of 6,300 dyadic dialogue sessions between 694 pairs of speakers with 53,126 utterances in total. We also construct session-level and pair-level relation classification tasks with widely-accepted baselines. The experimental results show that both tasks are challenging for existing models and the dataset will be useful for future research.
On complementing end-to-end human behavior predictors with planning

High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events.Planningbased prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift (as we verify in this work), but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal.In this work, we analyze one family of approaches that strive to get the best of both worlds: use the end-to-end predictor on common cases, but do not rely on it for tail events / out-of-distribution inputsswitch to the planning-based predictor there.We contribute an analysis of different approaches for detecting when to make this switch, using an autonomous driving domain.We find that promising approaches based on ensembling or generative modeling of the training distribution might not be reliable, but that there very simple methods which can perform surprisingly well -including training a classifier to pick up on tell-tale issues in predicted trajectories.
An Investigation of Suitability of Pre-Trained Language Models for Dialogue Generation – Avoiding Discrepancies

Pre-trained language models have been widely used in response generation for open-domain dialogue.These approaches are built within 4 frameworks: Transformer-ED, Transformer-Dec, Transformer-MLM and Transformer-AR.In this study, we experimentally compare them using both large and small-scale data.This reveals that decoder-only architecture is better than stacked encoder-decoder, and both leftto-right and bi-directional attention have their own advantages.We further define two concepts of model discrepancy, which provides a new explanation to the model performance.As discrepancies may hinder performance, we propose two solutions to reduce them, which successfully improve the model performance.
Inspecting the concept knowledge graph encoded by modern language models

The field of natural language understanding has experienced exponential progress in the last few years, with impressive results in several tasks.This success has motivated researchers to study the underlying knowledge encoded by these models.Despite this, attempts to understand their semantic capabilities have not been successful, often leading to non-conclusive, or contradictory conclusions among different works.Via a probing classifier, we extract the underlying knowledge graph of nine of the most influential language models of the last years, including word embeddings, text generators, and context encoders.This probe is based on concept relatedness, grounded on WordNet.Our results reveal that all the models encode this knowledge, but suffer from several inaccuracies.Furthermore, we show that the different architectures and training strategies lead to different model biases.We conduct a systematic evaluation to discover specific factors that explain why some concepts are challenging.We hope our insights will motivate the development of models that capture concepts more precisely.
DefSent: Sentence Embeddings using Definition Sentences

Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks.However, these methods are only available for limited languages due to relying heavily on the large NLI datasets.In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary, which performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks than conventional methods.Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets.We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets.
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training.Separately, certified word substitution robustness methods have been developed to decrease the impact of spurious features and synonym substitutions on model predictions.While their end goals are different, they both aim to encourage models to make the same prediction for certain changes in the input.In this paper, we investigate the utility of certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks.We observe that certified robustness methods improve fairness, and using both robustness and bias mitigation methods in training results in an improvement in both fronts.
Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to Its Embedding

Graph neural networks (GNNs) are a class of powerful machine learning tools that model node relations for making predictions of nodes or links. GNN developers rely on quantitative metrics of the predictions to evaluate a GNN, but similar to many other neural networks, it is difficult for them to understand if the GNN truly learns characteristics of a graph as expected. We propose an approach to corresponding an input graph to its node embedding (aka latent space), a common component of GNNs that is later used for prediction. We abstract the data and tasks, and develop an interactive multi-view interface called CorGIE to instantiate the abstraction. As the key function in CorGIE, we propose the K-hop graph layout to show topological neighbors in hops and their clustering structure. To evaluate the functionality and usability of CorGIE, we present how to use CorGIE in two usage scenarios, and conduct a case study with five GNN experts. Availability: Open-source code at https://github.com/zipengliu/corgie-ui/, supplemental materials & video at https://osf.io/tr3sb/.
Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset

This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks. The quantizer performance is investigated in a wide dynamic range of data variances, and for that purpose, we derive novel closed-form expressions. Moreover, we propose two selection criteria for the variance range of interest. Binary quantizers are further implemented for compressing neural network weights and its performance is analysed for a simple classification task. Good matching between theory and experiment is observed and a great possibility for implementation is indicated.
How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?

While non-autoregressive (NAR) models are showing great promise for machine translation (MT), their use is limited by their dependence on knowledge distillation from autoregressive models.To address this issue, we seek to understand why distillation is so effective.Prior work suggests that distilled training data is less complex than manual translations.Based on experiments with the Levenshtein Transformer and the Mask-Predict NAR models on the WMT14 German-English task, this paper shows that different types of complexity have different impacts: while reducing lexical diversity and decreasing reordering complexity both help NAR learn better alignment between source and target, and thus improve translation quality, lexical diversity is the main reason why distillation increases model confidence, which affects the calibration of different NAR models differently.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving

Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to obviate the need for such heuristics, however, its deployment in automated theorem proving remains a challenge. In this paper we introduce TRAIL, a system that applies deep reinforcement learning to saturation-based theorem proving. TRAIL leverages (a) a novel neural representation of the state of a theorem prover and (b) a novel characterization of the inference selection process in terms of an attention-based action policy. We show through systematic analysis that these mechanisms allow TRAIL to significantly outperform previous reinforcement-learning-based theorem provers on two benchmark datasets for first-order logic automated theorem proving (proving around 15% more theorems).
Automatically Select Emotion for Response via Personality-affected Emotion Transition

To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans.However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked.This may lead to inconsistent emotional expressions and disinterest users.To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation.In detail, the emotion of the dialog system is transitioned from its preceding emotion in context.The transition is triggered by the preceding dialog context and affected by the specified personality trait.To achieve this, we first model the emotion transition in the dialog system as the variation between the preceding emotion and the response emotion in the Valence-Arousal-Dominance (VAD) emotion space.Then, we design neural networks to encode the preceding dialog context and the specified personality traits to compose the variation.Finally, the emotion for response is selected from the sum of the preceding emotion and the variation.We construct a dialog dataset with emotion and personality labels and conduct emotion prediction tasks for evaluation.Experimental results validate the effectiveness of the personality-affected emotion transition. 1 .
Prioritise the Best Variation

Binary session types guarantee communication safety and session fidelity, but alone they cannot rule out deadlocks arising from the interleaving of different sessions. In Classical Processes (CP) [53]—a process calculus based on classical linear logic—deadlock freedom is guaranteed by combining channel creation and parallel composition under the same logical cut rule. Similarly, in Good Variation (GV) [39, 54]—a linear concurrent $$\lambda $$ -calculus—deadlock freedom is guaranteed by combining channel creation and thread spawning under the same operation, called fork. In both CP and GV, deadlock freedom is achieved at the expense of expressivity, as the only processes allowed are tree-structured. Dardha and Gay [19] define Priority CP (PCP), which allows cyclic-structured processes and restores deadlock freedom by using priorities, in line with Kobayashi and Padovani [34, 44]. Following PCP, we present Priority GV (PGV), a variant of GV which decouples channel creation from thread spawning. Consequently, we type cyclic-structured processes and restore deadlock freedom by using priorities. We show that our type system is sound by proving subject reduction and progress. We define an encoding from PCP to PGV and prove that the encoding preserves typing and is sound and complete with respect to the operational semantics.
A CNN-Based Prediction-Aware Quality Enhancement Framework for VVC

This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal. The motivation is that normative decisions made by the encoder can significantly impact the type and strength of artifacts in the decoded images. In this paper, the main focus has been put on decisions defining the prediction signal in intra and inter frames. This information has been used in the training phase as well as input to help the process of learning artifacts that are specific to each coding type. Furthermore, to retain a low memory requirement for the proposed method, one model is used for all Quantization Parameters (QPs) with a Quantization Parameter (QP)-map, which is also shared between luma and chroma components. In addition to the Post Processing (PP) approach, the In-Loop Filtering (ILF) codec integration has also been considered, where the characteristics of the Group of Pictures (GoP) are taken into account to boost the performance. The proposed CNN-based Quality Enhancement (QE) framework has been implemented on top of the Versatile Video Coding (VVC) Test Model (VTM-10). Experiments show that the prediction-aware aspect of the proposed method improves the coding efficiency gain of the default CNN-based QE method by 1.52%, in terms of BD-BR, at the same network complexity compared to the default CNN-based QE filter.
BACO: A Background Knowledge- and Content-Based Framework for Citing Sentence Generation

In this paper, we focus on the problem of citing sentence generation, which entails generating a short text to capture the salient information in a cited paper and the connection between the citing and cited paper.We present BACO, a BAckground knowledgeand COntent-based framework for citing sentence generation, which considers two types of information: (1) background knowledge by leveraging structural information from a citation network; and (2) content, which represents in-depth information about what to cite and why to cite.First, a citation network is encoded to provide background knowledge.Second, we apply salience estimation to identify what to cite by estimating the importance of sentences in the cited paper.During the decoding stage, both types of information are combined to facilitate the text generation.We then conduct joint training of the generator and citation function classification to make the model aware of why to cite.Our experimental results show that our framework outperforms comparative baselines.
A Feature Selection Method by using Chaotic Cuckoo Search Optimization Algorithm with Elitist Preservation and Uniform Mutation for Data Classification

Feature selection is an essential step in the preprocessing of data in pattern recognition and data mining. Nowadays, the feature selection problem as an optimization problem can be solved with nature-inspired algorithm. In this paper, we propose an efficient feature selection method based on the cuckoo search algorithm called CBCSEM. The proposed method avoids the premature convergence of traditional methods and the tendency to fall into local optima, and this efficient method is attributed to three aspects. Firstly, the chaotic map increases the diversity of the initialization of the algorithm and lays the foundation for its convergence. Then, the proposed two-population elite preservation strategy can find the attractive one of each generation and preserve it. Finally, Lévy flight is developed to update the position of a cuckoo, and the proposed uniform mutation strategy avoids the trouble that the search space is too large for the convergence of the algorithm due to Lévy flight and improves the algorithm exploitation ability. The experimental results on several real UCI datasets show that the proposed method is competitive in comparison with other feature selection algorithms.
Field Testing of Mixed Reality (MR) Technologies for Quality Control of As-Built Models at Project Handover: A Case Study

Current as-built BIMs generated during design and construction and delivered to owners at project handover do not fulfill all FM needs. To provide value, an FM-Capable BIM is required that is optimized for operations and maintenance. Two of the requirements of an FM-capable BIM is that it serves as a central repository for accurate, complete, and reliable data, and it provides accurate and complete representation of building components and systems. This paper reports on a collaborative project to evaluate the usability and effectiveness of the Hololens with the VisualLive HoloLive platform to test the accuracy and completeness of model graphical and non-graphical information. A Navisworks as-built BIM of the mechanical room of a classroom building was used. The paper also outlines current Mixed Reality (MR) features, compares two MR platforms, HoloLive and Trimble Connect, and provides future research direction. Results indicated that the usability and effectiveness of MR technologies are adequate to verify model data and test quality of the BIM graphics. However, the users had difficulties with typing, dependency on WiFi signals, visibility of distant objects, objects in low lighting areas and objects with dull colors. Concerns regarding the wearability and safety of the glasses were highlighted.
Quantum Local Search with the Quantum Alternating Operator Ansatz

We present a new hybrid, local search algorithm for quantum approximate optimization of constrained combinatorial optimization problems. We focus on the Maximum Independent Set problem and demonstrate the ability of quantum local search to solve large problem instances on quantum devices with few qubits. This hybrid algorithm iteratively finds independent sets over carefully constructed neighborhoods and combines these solutions to obtain a global solution. We study the performance of this algorithm on 3-regular, Community, and Erdős-Rényi graphs with up to 100 nodes.
Modeling communication of collaborative multiagent system under epistemic planning

In most multiagent applications, communication is essential among agents to coordinate their actions and achieve their goals. However, communication often has a related cost that affects overall system performance. In this paper, we draw inspiration from epistemic planning studies to develop a communication model for agents that allows them to cooperate and make communication decisions effectively within a planning task. The proposed model treats a communication process as an action that modifies the epistemic state of the team. We evaluate whether agents can cooperate effectively and achieve higher performance using communication protocol modeled in our epistemic planning framework in two simulated tasks. Based on an empirical study conducted using search and rescue tasks with different scenarios, our results show that the proposed model improved team performance across all scenarios than baseline models.
Mixer-phaser Ansätze for quantum optimization with hard constraints

Abstract We introduce multiple parametrized circuit ansätze and present the results of a numerical study comparing their performance with a standard Quantum Alternating Operator Ansatz approach. The ansätze are inspired by mixing and phase separation in the QAOA, and also motivated by compilation considerations with the aim of running on near-term superconducting quantum processors. The methods are tested on random instances of a quadratic binary constrained optimization problem that is fully connected for which the space of feasible solutions has constant Hamming weight. For the parameter setting strategies and evaluation metric used, the average performance achieved by the QAOA is effectively matched by the one obtained by a ”mixer-phaser” ansatz that can be compiled in less than half-depth of standard QAOA on most superconducting qubit processors.
Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling

Statistical inference based on the cluster weighted model often requires some subjective judgment from the modeler. Many features influence the final solution, such as the number of mixture components, the shape of the clusters in the explanatory variables, and the degree of heteroscedasticity of the errors around the regression lines. Moreover, to deal with outliers and contamination that may appear in the data, hyper-parameter values ensuring robust estimation are also needed. In principle, this freedom gives rise to a variety of “legitimate” solutions, each derived by a specific set of choices and their implications in modeling. Here we introduce a method for identifying a “set of good models” to cluster a dataset, considering the whole panorama of choices. In this way, we enable the practitioner, or the scientist who needs to cluster the data, to make an educated choice. They will be able to identify the most appropriate solutions for the purposes of their own analysis, in light of their stability and validity.