Reviewerly/exHarmony huggingface dataset:
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes.However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts.Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions).To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes.Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed.Our study opens a new research direction for controllable text generation and enables the development of attributeentangled CTG models.Our dataset and source codes are available at https://github.com/huangshulin123/Blessing-Generation.
RedApt: An Adaptor for wav2vec 2 EncodingFaster and Smaller Speech Translation without Quality Compromise

Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-theart (SotA) results; yet, using such encoders is computationally expensive.To improve this, we present a novel Reducer Adaptor block, RedApt, that could be seamlessly integrated within any Transformer-based speech encoding architecture.Integrating the pretrained WAV2VEC 2 speech encoder with RedApt brings 41% speedup, 33% memory reduction with 24% fewer FLOPs at inference.To our positive surprise, our ST model with RedApt outperforms the SotA architecture by an average of 0.68 BLEU score on 8 language pairs from Must-C.
Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering

Recent work on non-autoregressive neural machine translation (NAT) that leverages alignment information to explicitly reduce the modality of target distribution has reported comparable performance with counterparts that tackle multi-modality problem by implicitly modeling dependencies.Effectiveness in handling alignment is vital for models that follow this approach, where a token reordering mechanism is typically involved and plays a vital role.We review the reordering capability of the respective mechanisms in recent NAT models, and our experimental results show that their performance is sub-optimal.We propose to learn a non-autoregressive language model (NALM) based on transformer which can be combined with Viterbi decoding to achieve better reordering performance.We evaluate the proposed NALM using the PTB dataset where sentences with words permuted in different ways are expected to have their ordering recovered.Our empirical results show that the proposed method can outperform the stateof-the-art reordering mechanisms under different word permutation settings, with a 2-27 BLEU improvement, suggesting high potential for word alignment in NAT.
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data

Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error-prone implicit calculation.In this paper, we present TACUBE, to precompute aggregation/arithmetic results for the table in advance, so that they are handy and readily available for PLMs to answer numerical reasoning questions.TACUBE systematically and comprehensively covers a collection of computational operations over table segments.By simply concatenating TACUBE to the input sequence of PLMs, it shows significant experimental effectiveness.TACUBE promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new state-of-the-art results on WikiTQ (59.6% denotation accuracy).TACUBE 's improvements on numerical reasoning cases are even more notable: on TAT-QA, TACUBE promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5% on average, 36.6% on subtraction and 22.2% on division.We believe that TACUBE is a general and portable pre-computation solution that can be potentially integrated into various numerical reasoning frameworks.Data and code will be available at https://github.com/microsoft/TaCube.
SPE: Symmetrical Prompt Enhancement for Fact Probing

Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretraining (Petroni et al., 2019).Recent works probe PLMs for the extent of this knowledge through prompts either in discrete or continuous forms.However, these methods do not consider symmetry of the task: object prediction and subject prediction.In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.Our results on a popular factual probing dataset, LAMA, show significant improvement of SPE over previous probing methods.
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation

The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently.In one vein of study, researchers are interested in understanding why and how transformers work.In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable.In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns.Then those patterns are injected, not only to smaller models, but also to the original model.The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation.After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency.
Self-supervised Rewiring of Pre-trained Speech Encoders:Towards Faster Fine-tuning with Less Labels in Speech Processing

Pre-trained speech encoders have facilitated great success across various speech processing tasks.However, fine-tuning these encoders for downstream tasks require sufficiently large training data to converge or to achieve stateof-the-art.In text domain this has been partly attributed to sub-optimality of the representation space in pre-trained Transformers.In this work, we take a sober look into pre-trained speech encoders and rewire their representation space without requiring any task-specific labels.Our method utilises neutrally synthesised version of audio inputs along with frame masking to construct positive pairs for contrastive self-supervised learning.When it is used for augmenting the WAV2VEC 2 encoder, we observe consistent improvement of isotropy in the representation space.Our experiments on 6 speech processing tasks, exhibit a significant convergence speedup during task fine-tuning as well as consistent task improvement, specially in low-resource settings.
Entity-level Sentiment Analysis in Contact Center Telephone Conversations

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text.It is very useful in a business context to understand user emotions towards certain entities, such as products or companies.In this paper, we demonstrate how we developed an entitylevel sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight.We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.
DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents

In the real world, autonomous driving agents navigate in highly dynamic environments full of unexpected situations where pre-trained models are unreliable.In these situations, what is immediately available to vehicles is often only human operators.Empowering autonomous driving agents with the ability to navigate in a continuous and dynamic environment and to communicate with humans through sensorimotor-grounded dialogue becomes critical.To this end, we introduce Dialogue On the ROad To Handle Irregular Events (DOROTHIE), a novel interactive simulation platform that enables the creation of unexpected situations on the fly to support empirical studies on situated communication with autonomous driving agents.Based on this platform, we created the Situated Dialogue Navigation (SDN), a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams and 2.9 hours of trimmed audio.SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.We further developed a transformer-based baseline model for these SDN tasks.Our empirical results indicate that language guided-navigation in a highly dynamic environment is an extremely difficult task for end-to-end models.These results will provide insight towards future work on robust autonomous driving agents 1 .
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation

We introduce a new method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR).Compared with the original textual input, AMR is a structured semantic representation that presents the core concepts and relations in a sentence explicitly and unambiguously.It also helps reduce surface variations across different expressions and languages.Unlike most prior work that only evaluates the ability to measure semantic similarity, we present a thorough evaluation of existing multilingual sentence embeddings and our improved versions, which include a collection of five transfer tasks in different downstream applications.Experiment results show that retrofitting multilingual sentence embeddings with AMR leads to better state-of-the-art performance on both semantic textual similarity and transfer tasks.Our codebase and evaluation scripts can be found at https://github.com/jcyk/MSE-AMR.
Answer Quality Aware Aggregation for Extractive QA Crowdsourcing

Quality control is essential for creating extractive question answering (EQA) datasets via crowdsourcing.Aggregation across answers, i.e. word spans within passages annotated, by different crowd workers is one major focus for ensuring its quality.However, crowd workers cannot reach a consensus on a considerable portion of questions.We introduce a simple yet effective answer aggregation method that takes into account the relations among the answer, question, and context passage.We evaluate answer quality from both the view of question answering model to determine how confident the QA model is about each answer and the view of the answer verification model to determine whether the answer is correct.Then we compute aggregation scores with each answer's quality and its contextual embedding produced by pre-trained language models.The experiments on a large real crowdsourced EQA dataset show that our framework outperforms baselines by around 16% on precision and effectively conduct answer aggregation for extractive QA task.The code is available at https://github.com/zpeide/Answer-Quality-Aware
Contextualizing Language Models for Norms Diverging from Social Majority

To comprehensibly contextualize decisions, artificial systems in social situations need a high degree of awareness of the rules of conduct of human behavior.Especially transformer-based language models have recently been shown to exhibit some such awareness.But what if norms in some social setting do not adhere to or even blatantly deviate from the mainstream?In this paper, we introduce a novel mechanism based on deontic logic to allow for a flexible adaptation of individual norms by de-biasing training data sets and a task-reduction to textual entailment.Building on the popular 'Moral Stories' dataset we on the one hand highlight the intrinsic bias of current language models, on the other hand characterize the adaptability of pre-trained models to deviating norms in fine-tuning settings. 1
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access

To alleviate the problem of structured databases' limited coverage, recent taskoriented dialogue systems incorporate external unstructured knowledge to guide the generation of system responses.However, these usually use word or sentence level similarities to detect the relevant knowledge context, which only partially capture the topical level relevance.In this paper, we examine how to better integrate topical information in knowledge grounded task-oriented dialogue and propose "Topic-Aware Response Generation" (TARG), an endto-end response generation model.TARG incorporates multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources towards a better understanding of the dialogue history.Experimental results indicate that TARG achieves state-of-theart performance in knowledge selection and response generation, outperforming previous state-of-the-art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial, and performing comparably with previous work on DSTC9; both being knowledge-grounded task-oriented dialogue datasets.
Focus! Relevant and Sufficient Context Selection for News Image Captioning

News Image Captioning requires describing an image by leveraging additional context from a news article.Previous works only coarsely leverage the article to extract the necessary context, which makes it challenging for models to identify relevant events and named entities.In our paper, we first demonstrate that by combining more fine-grained context that captures the key named entities (obtained via an oracle) and the global context that summarizes the news, we can dramatically improve the model's ability to generate accurate news captions.This begs the question, how to automatically extract such key entities from an image?We propose to use the pre-trained vision and language retrieval model CLIP to localize the visually grounded entities in the news article and then capture the non-visual entities via an open relation extraction model.Our experiments demonstrate that by simply selecting a better context from the article, we can significantly improve the performance of existing models and achieve new state-of-the-art performance on multiple benchmarks. Serena Williams and Andy Murray in Wimbledon Mixed Doubles: It's HappeningCaption: Andy Murray practicing at the All England Club on Tuesday
A Lucid Analysis of the New Education Policy 2020

Purpose: To understand in a nutshell educational policy carried out after the independence of India. To accumulate information about educational reforms carried out in the last seven decades. To interpret the problems of the education sector. To find the existing gap between educational delivery and market demand. To find opportunities and avenues to effectively implement the new education policy in 2020. Research Design: The study uses descriptive research design by using secondary data including research articles published in international journals and books published from 2012 to 2023, which are available for search in the google scholar database. Result/Outcome/Findings of the Study: The paper highlights various educational policies and their lacunas. The study elucidates the evolution of education policies, a comparative analysis of various policies and ways forward to implement National Education Policy (NEP) 2020. The SWOC analysis of NEP determines the existing gap and suggests measures for the country's effective inception, adoption and implementation of NEP 2020. Originality/Value: The selection of relevant content for exploration based on the literature survey, segregation of dimensions of old educational policies based on the literature survey and SWOC analysis upon the New Education Policy 2020 is unique in content, review and analysis bringing originality to the derived conclusions. Paper type: A Case Study
Developments and Directions in Intellectual Property Law: 20 Years of the IPKat

Journal Article Book review: ‘Developments and Directions in Intellectual Property Law: 20 Years of the IPKat’ Get access William Patry William Patry Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, jpad066, https://doi.org/10.1093/jiplp/jpad066 Published: 04 August 2023 Article history Received: 25 June 2023 Editorial decision: 16 July 2023 Published: 04 August 2023 Corrected and typeset: 07 August 2023
Does judicial informatization affect firms’ digital innovation intention during the COVID-19 pandemic?

ABSTRACTUsing firm-level data in China from 2020 to 2021, this study uses text analysis and machine learning methods to construct an index of corporate digital innovation and examines the connection between judicial informatization and firms’ digital innovation during the COVID-19 pandemic. Our estimation finds that judicial informatization contributes to increasing firms’ digital innovation, especially in the high-tech industry. The results remain unchanged under a series of robustness tests. These findings extend the existing research on the impact of judicial informatization and the determinants of firms’ digital innovation, providing reference for firm executives and policy makers.KEYWORDS: COVID-19judicial informatizationdigital innovationChinaJEL CLASSIFICATION: K30O34O39 AcknowledgementsWe appreciate the financial supports from the Humanities and Social Sciences Planning Project of the Ministry of Education, China (Grant No. 20YJA790098), and the Key Projects of the National Social Science Foundation, China (Grant No. 21AZD005).Disclosure statementNo potential conflict of interest was reported by the author(s).Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/13504851.2023.2244229
Mobile Training Unit for Increasing the Quality of Vocational High Schools

The demand to produce graduates with both soft and hard skills is a challenge for vocational high schools to continue innovating. The Mobile Training Unit (MTU) is a program the Aceh government uses to achieve these demands and overcome classic problems in vocational high school teaching, such as a lack of facilities or human resources. This study aims to analyze the Mobile Training Unit for Improving the Quality of Vocational High Schools. This type of research is qualitative with a descriptive approach. The population of this study were three SMKs that had been previously selected together with the MTU program committee. The methods used in collecting data are observation, semi-structured interview guidelines, and documentation. Data collection instrument using a questionnaire. The technique used to analyze the data is a descriptive qualitative analysis using a triangulation process. The research results are that the Mobile Training Unit must be prepared based on the needs of students and mayors in SMK. Determining priorities to become participants, careful preparation and implementation impact improving the quality of SMKs and the motivation of students who need sufficient training. It was concluded that MTU is carried out with careful planning and processes and requires high collaboration and consistency between the government, schools and students.
Pengembangan Media Pembelajaran Melalui Website Berbasis Masalah Materi Sistem Reproduksi Kelas IX MTS Negeri Mamuju

This study aim to Development Of Learning Media Through Website Problem Based Learning that is valid, practical, and effective. This study is a research and development with ADDIE development models, which consists of analyze, design, development, emplementation, and evaluation. The participant of this study was The Ninth (9th) Grade MTs Negeri 1 Mamuju with a total of 30 participants. The instruments used in this study were validation sheets to collect data validity of Development Of Learning Media Through Website Problem Based about content and product, observation sheets to collect data about practically, teacher and student response quetionnaires, and test on student result study. The Development Of Learning Media Through Website Problem Based Learning Materials Of Reproductive System were developed yhen tested for validity until it was declared valid by validator and then it was implemented in the classroom to determine the practically through the implantation observation, teachers an students response quetionnaires also the effectiveness of this product thorugh students learning outcomes results. The results of the observation of implementation met the validity status. Teacher response toward the product were positif, Students response toward the product were positif. The resulst of student learning outcome met the criteria effectiveness criteria because there found the increase value of student learning outcome. This, it can be concluded that the The Development Of Learning Media Through Website Problem Based Learning Materials Of Reproductive System At The Ninth (9th) Grade MTs Negeri 1 Mamuju have met valid, practical, and effective criteria.
An approach to the systematic characterization of multitask accelerated CNN inference in edge MPSoCs

Deep Learning is today ubiquitous and is increasingly moving from the cloud down to the edge of networked infrastructures, where it enables embedded applications to perform complex inference tasks close to the data sources, reducing long-distance data movement and alleviating the need for a powerful cloud infrastructure. Edge-class MPSoC devices featuring an on-chip FPGA fabric offer key advantages for Deep Learning inference tasks, especially for complex applications where multiple models may be run concurrently in the same platform. In this work, we propose an approach and a practical framework for the systematic characterization of multithreaded Deep Learning inference on edge FPGA MPSoCs. We instantiate the framework into a real-world MPSoC platform, targeting Xilinx Vitis-AI as a representative example of a commercial Deep Learning acceleration toolkit for edge environments. We design a comprehensive experimental campaign and apply it to the platform for several CNNs, each trained on three different datasets. We show that our approach can be used for both hardware- and software-level analysis of a target system. Among other findings, the analysis revealed a suboptimal behaviour of the underlying toolkit runtime, involving the utilization of the accelerator cores and the uneven software latency of the support library, influenced by the shapes of the input tensors.
Chapter 18. Enhancing Case-Based Reasoning with Neural Networks

Integrations of case-based reasoning (CBR) with neural approaches are appealing because of their complimentary characteristics. This chapter presents research on neuro-symbolic integrations to support CBR, to reduce knowledge engineering and improve performance for CBR systems. It summarizes three strands of research: First, on extracting features for case retrieval from deep neural networks to use in concert with expert-generated features, second, on applying neural networks to learn to adapt the solutions of retrieved cases to fit new situations, and third, on harmonizing similarity learning with case adaptation learning, in order to focus retrieval on adaptable cases. It summarizes strengths, weaknesses and tradeoffs of these approaches, and points to future challenges for neuro-CBR integrations.