Reviewerly/exHarmony huggingface dataset:
disco: a toolkit for Distributional Control of Generative Models

Pre-trained language models and other generative models have revolutionized NLP and beyond.However, these models tend to reproduce undesirable biases present in their training data.Also, they may overlook patterns that are important but challenging to capture.To address these limitations, researchers have introduced distributional control techniques.These techniques, not limited to language, allow controlling the prevalence (i.e.expectations) of any features of interest in the model's outputs.Despite their potential, the widespread adoption of these techniques has been hindered by the difficulty in adapting the complex, disconnected code.Here, we present disco, an open-source Python library that brings these techniques to the broader public.
Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach

Currently, researchers focus on generating codes from the requirement documents.However, current approaches still perform poorly on some requirements needing complex problemsolving skills.In reality, to tackle such complex requirements, instead of directly translating requirement documents into codes, software engineers write codes via unified modeling language diagrams, such as flowcharts, an intermediate tool to analyze and visualize the system.Therefore, we propose a new source code generation task, that is, to generate source code from flowcharts with texts.We manually construct a benchmark dataset containing 320 flowcharts with their corresponding source codes 1 .Obviously, it is not straightforward to employ the current approaches for the new source code generation task since (1) the flowchart is a graph that contains various structures, including loop, selection, and others which is different from texts; (2) the connections between nodes in the flowchart are abundant and diverse which need to be carefully handled.To solve the above problems, we propose a two-stage code generation model.In the first stage, a structure recognition model is employed to transform the flowchart into pseudo-code containing the structural conventions of a typical programming language such as while, if.In the second stage, a code generation model is employed to convert the pseudo-code into code.Experimental results show that the proposed approach can achieve some improvement over the baselines.
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language

Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference.These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning.The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to supporting axioms) is significantly more efficient at proof-finding.Importing this intuition into the LM setting, we develop a Backward Chaining algorithm, called LAM-BADA, that decomposes reasoning into four sub-modules.These sub-modules are simply implemented by few-shot prompted LM inference.We show that LAMBADA achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text

Standard methods for multi-label text classification largely rely on encoder-only pretrained language models, whereas encoderdecoder models have proven more effective in other classification tasks.In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder.We carry out experiments on four datasets-two in the legal domain and two in the biomedical domain, each with two levels of label granularity-and always depart from the same pre-trained model, T5.Our results show that encoder-decoder methods outperform encoderonly methods, with a growing advantage on more complex datasets and labeling schemes of finer granularity.Using encoder-decoder models in a non-autoregressive fashion, in particular, yields the best performance overall, so we further study this approach through ablations to better understand its strengths.
DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to recognize the discourse relations between the arguments with the absence of discourse connectives.The sense labels for each discourse relation follow a hierarchical classification scheme in the annotation process (Prasad et al., 2008), forming a hierarchy structure.Most existing works do not well incorporate the hierarchy structure but focus on the syntax features and the prior knowledge of connectives in the manner of pure text classification.We argue that it is more effective to predict the paths inside the hierarchical tree (e.g., "Comparison -> Contrast -> however") rather than flat labels (e.g., Contrast) or connectives (e.g., however).We propose a prompt-based path prediction method to utilize the interactive information and intrinsic senses among the hierarchy in IDRR.This is the first work that injects such structure information into pre-trained language models via prompt tuning, and the performance of our solution shows significant and consistent improvement against competitive baselines.
SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller

In this paper, we propose a new task of subevent generation for an unseen process to evaluate the understanding of the coherence of subevent actions and objects.To solve the problem, we design SubeventWriter, a sub-event sequence generation framework with a coherence controller.Given an unseen process, the framework can iteratively construct the subevent sequence by generating one sub-event at each iteration.We also design a very effective coherence controller to decode more coherent sub-events.As our extensive experiments and analysis indicate, SubeventWriter 1 can generate more reliable and meaningful sub-event sequences for unseen processes.… 1 2 k … Initial Input: How to make a chocolate cake?Step 1: [M]
Multi-VQG: Generating Engaging Questions for Multiple Images

Generating engaging content has drawn much recent attention in the NLP community.Asking questions is a natural way to respond to photos and promote awareness.However, most answers to questions in traditional questionanswering (QA) datasets are factoids, which reduce individuals' willingness to answer.Furthermore, traditional visual question generation (VQG) confines the source data for question generation to single images, resulting in a limited ability to comprehend time-series information of the underlying event.In this paper, we propose generating engaging questions from multiple images.We present MVQG 1 , a new dataset, and establish a series of baselines, including both end-to-end and dual-stage architectures.Results show that building stories behind the image sequence enables models to generate engaging questions, which confirms our assumption that people typically construct a picture of the event in their minds before asking questions.These results open up an exciting challenge for visual-and-language models to implicitly construct a story behind a series of photos to allow for creativity and experience sharing and hence draw attention to downstream applications.How would you act if you found yourself in a room filled with cans of free drinks?Have you ever gone to beer tastings and where would that be at?How long did the cat lounge around in the book room?What would this cat sit on next?
Revisiting Pre-trained Language Models and their Evaluation for Arabic Natural Language Processing

There is a growing body of work in recent years to develop pre-trained language models (PLMs) for the Arabic language.This work addresses two major problems in existing Arabic PLMs that limit the progress of the Arabic NLU and NLG fields.First, existing Arabic PLMs are not well-explored and their pre-training can be improved significantly using a more methodical approach.Second, there is a lack of systematic and reproducible evaluation of these models in the literature.We revisit both the pre-training and evaluation of Arabic PLMs.In terms of pre-training, we explore the impact of the quality of the pretraining data, the size of the model and the incorporation of character-level information to Arabic PLMs.As a result, we release three new Arabic BERT-style models (JABER, Char-JABER, and SABER), and two T5-style models (AT5S and AT5B).In terms of evaluation, we conduct a comprehensive empirical study to systematically evaluate the performance of existing state-of-the-art models on ALUE, a leaderboard-powered benchmark for Arabic NLU tasks, and on a subset of Arabic generative tasks.We show that our models significantly outperform existing Arabic PLMs and achieve a new state-of-the-art performance on both discriminative and generative tasks.
Entailment Semantics Can Be Extracted from an Ideal Language Model

Language models are often trained on text alone, without additional grounding.There is debate as to how much of natural language semantics can be inferred from such a procedure.We prove that entailment judgments between sentences can be extracted from an ideal language model that has perfectly learned its target distribution, assuming the training sentences are generated by Gricean agents, i.e., agents who follow fundamental principles of communication from the linguistic theory of pragmatics.We also show entailment judgments can be decoded from the predictions of a language model trained on such Gricean data.Our results reveal a pathway for understanding the semantic information encoded in unlabeled linguistic data and a potential framework for extracting semantics from language models.
An Explainable Toolbox for Evaluating Pre-trained Vision-Language Models

We introduce VL-CheckList, a toolbox for evaluating Vision-Language Pretraining (VLP) models, along with a benchmark dataset for fine-grained VLP model analysis.Most existing VLP models evaluate their performance by comparing the finetuned downstream task performance.However, only average downstream task accuracy provides little information about the pros and cons of each VLP method.In this paper, we demonstrate how minor input changes in language and vision will affect the prediction outputs.We also provide a guideline for the research community to utilizes and contributes to this toolbox.Lastly, a case study based on VL-CheckList is conducted to analyze one of the representative VLP models.
Discovering Language-neutral Sub-networks in Multilingual Language Models

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and the effect of such representations on cross-lingual transfer performance, remain open questions.In this work, we conceptualize language neutrality of multilingual models as a function of the overlap between language-encoding sub-networks of these models. We employ the lottery ticket hypothesis to discover sub-networks that are individually optimized for various languages and tasks. Our evaluation across three distinct tasks and eleven typologically-diverse languages demonstrates that sub-networks for different languages are topologically similar (i.e., language-neutral), making them effective initializations for cross-lingual transfer with limited performance degradation.
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation

Commonsense reasoning tasks such as commonsense knowledge graph completion and commonsense question answering require powerful representation learning.In this paper, we propose to learn commonsense knowledge representation by MICO, a Multi-alternative con-trastIve learning framework on COmmonsense knowledge graphs (MICO).MICO generates the commonsense knowledge representation by contextual interaction between entity nodes and relations with multi-alternative contrastive learning.In MICO, the head and tail entities in an (h, r, t) knowledge triple are converted to two relation-aware sequence pairs (a premise and an alternative) in the form of natural language.Semantic representations generated by MICO can benefit the following two tasks by simply comparing the distance score between the representations: 1) zero-shot commonsense question answering task; 2) inductive commonsense knowledge graph completion task.Extensive experiments show the effectiveness of our method.
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA).To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR.Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines.The transformation matrix is finetuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors.Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multianswer setting.Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning.Our code and models can be found at https://github.com/LuJunru/TranCLR.
Adaptive Visual Sentiment Prediction Model Based on Event Concepts and Object Detection Techniques in Social Media

Now-a-days, the increasing number of smartphones has caused the immediate sharing of photographs capturing current events on social media.The sentimental content of pictures from social events starts to be obtained from visual material, so visual sentiment analysis is a vital research topic.The research aims to reach valuable criteria to modify the visual sentiment prediction model based on event concepts and object detection techniques.In addition to adapting the approach for designing the method for predicting visual sentiments in a social network according to concept scores and measuring the performance of the model for predicting visual sentiments as accurately as possible, approach obtains a visual summary of social event images based on the visual elements that appear in the pictures which exceed sentiment-specific features.By this method, attributes (color, texture) are assigned to sentiments with discovering affective objects that are used to obtain emotions related to a picture of a social event by mapping the top predicted qualities to feelings and extracting the prevailing emotion connected with a photograph of a social event.This method is valid for a wide range of social events.This strategy also demonstrates the social event's effectiveness for a difficult social event image collection by using techniques for classifying complicated event images into sentiments, whether positive or negative.
A Comparative Analysis of Methods for Detecting and Diagnosing Breast Cancer Based on Data Mining

Breast cancer is a significant public health concern worldwide, and early detection is crucial for its treatment. Although breast cancer has been extensively studied, there is still room for improvement in its classification accuracy. This study aims to improve the classification accuracy of breast cancer by applying information gain feature selection and machine learning techniques to the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The information gain method is utilized to reduce feature characteristics, and machine learning algorithms such as support vector machine (SVM), naive Bayes (NB), and C4.5 decision tree are employed for breast cancer classification. The study also conducts a comparison analysis based on accuracy value. The proposed model achieves maximum classification accuracy (100%) and a weighted average for precision (100%) and recall (100%) using a C4.5 decision tree, while SVM accuracy (98.42%) and weighted average for precision (98.17%) and recall (98.58%) are achieved using a C4.5 decision tree. The NB algorithm attains an accuracy of 96%, with a weighted average for precision (18.57%) and recall (50%). The proposed model's results are compared to similar studies and demonstrate significant progress, indicating new opportunities for breast cancer detection.
A Deep Learning System for Domain-Specific Speech Recognition

As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-theart automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with pre-trained DeepSpeech2 and Wav2Vec2 acoustic models to develop benefit-specific ASR systems. The domainspecific data are collected using proposed semi-supervised learning annotation with little human intervention. The best performance comes from a fine-tuned Wav2Vec2-Large-LV60 acoustic model with an external KenLM, which surpasses the Google and AWS ASR systems on benefit-specific speech. The viability of using error prone ASR transcriptions as part of spoken language understanding (SLU) is also investigated. Results of a benefit-specific natural language understanding (NLU) task show that the domain-specific fine-tuned ASR system can outperform the commercial ASR systems even when its transcriptions have higher word error rate (WER), and the results between fine-tuned ASR and human transcriptions are similar
Self-Healing of Trotter Error in Digital Adiabatic State Preparation

Adiabatic time evolution can be used to prepare a complicated quantum many-body state from one that is easier to synthesize and Trotterization can be used to implement such an evolution digitally. The complex interplay between nonadiabaticity and digitization influences the infidelity of this process. We prove that the first-order Trotterization of a complete adiabatic evolution has a cumulative infidelity that scales as O(T^{-2}δt^{2}) instead of O(T^{2}δt^{2}) expected from general Trotter error bounds, where δt is the time step and T is the total time. This result suggests a self-healing mechanism and explains why, despite increasing T, infidelities for fixed-δt digitized evolutions still decrease for a wide variety of Hamiltonians. It also establishes a correspondence between the quantum approximate optimization algorithm and digitized quantum annealing.
Learning with Physical Rounding for Linear and Quadratic Leakage Functions

Fresh re-keying is a countermeasure against side-channel analysis where an ephemeral key is derived from a long-term key using a public random value. Popular instances of such schemes rely on key-homomorphic primitives, so that the re-keying process is easy to mask and the rest of the (e.g., block cipher) computations can run with cheaper countermeasures. The main requirement for these schemes to be secure is that the leakages of the ephemeral keys do not allow recovering the long-term key. The Learning with Physical Rounding (LWPR) problem formalizes this security in a practically-relevant model where the adversary can observe noise-free leakages. It can be viewed as a physical version of the Learning With Rounding (LWR) problem, where the rounding is performed by a leakage function and therefore does not have to be computed explicitly. In this paper, we first consolidate the intuition that LWPR cannot be secure in a serial implementation context without additional countermeasures (like shuffling), due to attacks exploiting worst-case leakages that can be mounted with practical data complexity. We then extend the understanding of LWPR in a parallel implementation setting. On the one hand, we generalize its robustness against cryptanalysis taking advantage of any (i.e., not only worst-case) leakage. A previous work claimed security in the specific context of a Hamming weight leakage function. We clarify necessary conditions to maintain this guarantee, based on the degree of the leakage function and the accuracy of its coefficients. On the other hand, we show that parallelism inherently provides good security against attacks exploiting worst-case leakages. We finally confirm the practical relevance of these findings by validating our assumptions experimentally for an exemplary implementation.
All‐Optical Data Processing with Photon‐Avalanching Nanocrystalline Photonic Synapse

Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.