Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of An Internet Provider Company Based on Twitter Using Support Vector Machine and Naïve Bayes Method

Tweets from users in the form of opinions about a product can be used as a company evaluation of the product. To obtain this evaluation, the method that can be used is sentiment analysis to divide opinions into positive and negative opinions. This study uses 1000 data from Twitter related to an internet service provider company where the data is divided into two classes, namely 692 positive classes and 308 negative classes. In the Tweet there are still many words that are not standard. Therefore, previously carried out the initial process or preprocessing to filter out non-standard words. Before doing the classification, the data needs to be divided into training data and test data with a ratio of 90:10, then processed using the Support Vector Machine and Naïve Bayes techniques to get the results of the classification of positive opinions and negative opinions. The level of accuracy in the classification using the Support Vector Machine is 84% ​​and using Naïve Bayes is 82%.
The Influence of KMS Trello on the Intern Performance CDC UIN Jakarta

Trello is software used for knowledge and project management. The ease of appearance and integration with Google Drive makes the UIN Jakarta Career Center interested in using Trello as a workspace for their intern. The problem of this research stems from the number of internals with non-technological backgrounds and this is the first time using this tool. So the purpose of this study is to see the effect of Trello software on internal performance and to see the difference in influence between technology and non-technological internals by using a research method, namely a descriptive qualitative approach through interviews with several selected sources
Temporal Logic Robot Task Planning with Active Acquisition of Information

This paper proposes a mission planning algorithm for the robot to operate in an environment with interference. The robot is equipped with proximity sensor to roughly estimate the environment and can apply active perception action to detect the environment. The goal of the robots is to accomplish complex tasks, captured by co-safe Linear Temporal Logic (scLTL) formulas. With the interference created by environment, general observation-based solution will cause a huge cost even if it can complete the task. When the robot without active perception encounters a set of states that the proximity observation can not tell, it will choose a conservative but costlier action. We aim to design an active perception plan for the robot to offset the impact which can reduce the cost. Our algorithm can trade off security and cost, to find a strategy to accomplish the complex task. We illustrate our method on motion planning case studies and show that the proposed algorithm can address complex planning tasks with smaller costs through active perception action.
Research on Online Teaching Practice of Smart Vocational Education Cloud Class in Higher Vocational Education

With the continuous development of modern information and the integration of internet technology into all walks of life,the integration of information-based classroom teaching technology into the actual teaching has become the focus of current research in higher vocational colleges.Smart cloud classroom online teaching in vocational education is a new teaching mode derived from the Internet environment.During the epidemic period,a series of information-based teaching technologies,such as online teaching and”classroom in the air”,provided strong support for the”suspension of classes and non-stop learning”of teaching work.In view of the in-depth research and discussion on the practical content of online teaching in higher vocational smart cloud classroom,this paper hopes to provide reference for future information teaching.
AI layer reference generator engine for early enablement of workloads over multiple frameworks

Compute operators/kernels (like Convolution, Relu, Gemm etc.) form the backbone of Deep Neural Network (DNN) models. With the advent of AI accelerators, there is a need to have specialized and optimized kernels for these operators to take full advantage of the features provided by the hardware. Kernels are mostly developed at High level languages like C, C++. They should work with almost the same accuracy and functionality as they would work on CPU/GPU machine. There are various ways listed below to check kernel accuracy and functionality. One of the common ways is to use Python interface provided by the different AI/ML frameworks. However, there are certain disadvantages like script level exposure which requires full stack integration and lack of multithreading because of python global interpreter lock. Another way is to develop a reference generator engine based on understanding of operators from frameworks. This strategy requires validating the correctness of the reference generator engine itself. Also, the generated reference could be at times in incompatible format and gets difficult to consume in the kernel validation code. In this paper we propose PyTeNet++ which stands for AI framework Pytorch[1], TensorFlow[2], Mxnet[3] using C++ APIs. The proposed solution is one of its kind and no such reference generator framework exists at the high-level language utilizing listed frameworks as backends. PyTeNet++ sits on top of C++ APIs exposed by these frameworks and helps to generate reference which could be seamlessly integrated with the kernel development/validation stack of the operators. It also helps to achieve maturity while kernel development. It exploits various benefits C++ provides like multithreading, low latency, high performance and OpenMP[4]. All these features are useful for faster kernel maturity. The smarter way of doing validation close to development environment helps in the early enablement (without full stack integration) of the DNN models, leading to quick maturity of the product and improved time-to-market.
Development of Interactive Teaching Materials Based on Android Application for Elementary School Students

This research aims to produce a prototype of interactive teaching materials based on android applications effectively used in thematic learning in Elementary School. This development research uses the DDD-E model with four stages of development which include: (1) decide, (2) design, (3) develop, and (4) evaluate. The products developed are divided into six applications that focus on the sub-theme of Environmental Conservation. Based on the test results, the obtained paired sample test results had a significance value of 0.001, which is less than 0.05. It can be interpreted that there is a significant difference between student learning outcomes before and after using interactive teaching material. The effectiveness of interactive teaching materials can be seen from the results of the N-Gain test with an average gain score of 0.6, which means the level of effectiveness of interactive teaching materials is in the medium category.
A Study of Touch Dynamics Biometrics Authentication

A lot of recent research activities have been fulfilled in keystroke and mouse dynamics in the past three decades but the work in touch screen biometric authentication still needs to be explored. The motivation of this paper is that touch dynamics behavioral biometric authentication can be easily deployed along with user normal activities. Behavioral biometric along with machine learning algorithms and risk-assessment techniques can be efficiently and effectively used as an adaptive authentication. With the rapid usage of smart phones and touch screens in other applications, touch dynamics can be an important authentication factor in multi-factor authentication. Though a numerous studies have been done in behavioral human biometric, there is indeed much scope in the area of touch dynamics. The objective of this study is to provide the throughout survey of techniques used in this field along with the advancements for further research work. Keywords Biometric, touch dynamics, Behavioral authentication, multi-factor authentication, adaptive authentication
Cheat Codes to Quantify Missing Source Information in Neural Machine Translation

This paper describes a method to quantify the amount of information H(t|s) added by the target sentence t that is not present in the source s in a neural machine translation system.We do this by providing the model the target sentence in a highly compressed form (a "cheat code"), and exploring the effect of the size of the cheat code.We find that the model is able to capture extra information from just a single float representation of the target and nearly reproduces the target with two 32-bit floats per target token.
Incremental Disfluency Detection for Spoken Learner English

Incremental disfluency detection provides a framework for computing communicative meaning from hesitations, repetitions and false starts commonly found in speech.One application of this area of research is in dialogue-based computer-assisted language learning (CALL), where detecting learners' production issues word-by-word can facilitate timely and pedagogically driven responses from an automated system.Existing research on disfluency detection in learner speech focuses on disfluency removal for subsequent downstream tasks, processing whole utterances non-incrementally.This paper instead explores the application of laughter as a feature for incremental disfluency detection and shows that when combined with silence, these features reduce the impact of learner errors on model precision as well as lead to an overall improvement of model performance.This work adds to the growing body of research incorporating laughter as a feature for dialogue processing tasks and provides further support for the application of multimodality in dialogue-based CALL systems.
Systematicity Emerges in Transformers when Abstract Grammatical Roles Guide Attention

Systematicity is thought to be a key inductive bias possessed by humans that is lacking in standard natural language processing systems such as those utilizing transformers.In this work, we investigate the extent to which the failure of transformers on systematic generalization tests can be attributed to a lack of linguistic abstraction in its attention mechanism.We develop a novel modification to the transformer by implementing two separate input streams: a role stream controls the attention distributions (i.e., queries and keys) at each layer, and a filler stream determines the values.Our results show that when abstract role labels are assigned to input sequences and provided to the role stream, systematic generalization is improved.
SURF: Semantic-level Unsupervised Reward Function for Machine Translation

The performance of Reinforcement Learning (RL) for natural language tasks including Machine Translation (MT) is crucially dependent on the reward formulation.This is due to the intrinsic difficulty of the task in the high-dimensional discrete action space as well as the sparseness of the standard reward functions defined for limited set of groundtruth sequences biased towards singular lexical choices.To address this issue, we formulate SURF, a maximally dense semantic-level unsupervised reward function which mimics human evaluation by considering both sentence fluency and semantic similarity.We demonstrate the strong potential of SURF to leverage a family of Actor-Critic Transformerbased Architectures with synchronous and asynchronous multi-agent variants.To tackle the problem of large action-state spaces, each agent is equipped with unique exploration strategies, promoting diversity during its exploration of the hypothesis space.When BLEU scores are compared, our dense unsupervised reward outperforms the standard sparse reward by 2% on average for in-and out-of-domain settings.
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations

We introduce a novel tree-based model that learns its composition function together with its structure.The architecture produces sentence embeddings by composing words according to an induced syntactic tree.The parsing and the composition functions are explicitly connected and, therefore, learned jointly.As a result, the sentence embedding is computed according to an interpretable linguistic pattern and may be used on any downstream task.We evaluate our encoder on downstream tasks, and we observe that it outperforms tree-based models relying on external parsers.In some configurations, it is even competitive with BERT base model.Our model is capable of supporting multiple parser architectures.We exploit this property to conduct an ablation study by comparing different parser initializations.We explore to which extent the trees produced by our model compare with linguistic structures and how this initialization impacts downstream performance.We empirically observe that downstream supervision troubles producing stable parses and preserving linguistically relevant structures.
DartmouthCS at SemEval-2022 Task 8: Predicting Multilingual News Article Similarity with Meta-Information and Translation

This paper presents our approach for tackling SemEval-2022 Task 8: Multilingual News Article Similarity. Our experiments show that even by using multi-lingual pre-trained language models (LMs), translating the text into the same language yields the best evaluation performance. We also find that stylometric features of the text and meta-information of the news articles can be predicted based on the text with low error rates, and these predictions could be used to improve the predictions of the overall similarity scores. These findings suggest substantial correlations between authorship information and topical similarity estimation, which sheds light on future stylometric and topic modeling research.
Data Augmentation for Low-Resource Dialogue Summarization

We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization.Our method generates synthetic examples by replacing text sections from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue.We utilize pretrained language models that produce highly likely dialogue alternatives while still being free to generate diverse alternatives.We applied our data augmentation method to the SAMSum dataset in low-resource scenarios, mimicking real-world problems such as chat, thread, and meeting summarization where large-scale supervised datasets with human-written summaries are scarce.Through both automatic and human evaluations, we show that DADS shows strong improvements for low-resource scenarios while generating topically diverse summaries without introducing additional hallucinations to the summaries.
Abstraction not Memory: BERT and the English Article System

Article prediction is a task that has long defied accurate linguistic description.As such, this task is ideally suited to evaluate models on their ability to emulate native-speaker intuition.To this end, we compare the performance of native English speakers and pre-trained models on the task of article prediction set up as a three way choice (a/an, the, zero).Our experiments with BERT show that BERT outperforms humans on this task across all articles.In particular, BERT is far superior to humans at detecting the zero article, possibly because we insert them using rules that the deep neural model can easily pick up.More interestingly, we find that BERT tends to agree more with annotators than with the corpus when inter-annotator agreement is high but switches to agreeing more with the corpus as inter-annotator agreement drops.We contend that this alignment with annotators, despite being trained on the corpus, suggests that BERT is not memorising article use, but captures a high level generalisation of article use akin to human intuition.
An Interactive Exploratory Tool for the Task of Hate Speech Detection

With the growth of Automatic Content Moderation (ACM) on widely used social media platforms, transparency into the design of moderation technology and policy is necessary for online communities to advocate for themselves when harms occur.In this work, we describe a suite of interactive modules to support the exploration of various aspects of this technology, and particularly of those components that rely on English models and datasets for hate speech detection, a subtask within ACM.We intend for this demo to support the various stakeholders of ACM in investigating the definitions and decisions that underpin current technologies such that those with technical knowledge and those with contextual knowledge may both better understand existing systems.
YNU-HPCC at SemEval-2022 Task 4: Finetuning Pretrained Language Models for Patronizing and Condescending Language Detection

This paper describes a system built for the SemEval-2022 competition.As participants in Task 4: Patronizing and Condescending Language Detection, we implemented the text sentiment classification system for two subtasks in English.Both subtasks involve determining emotions; subtask 1 requires us to determine whether the text belongs to the PCL category (single-label classification), and subtask 2 requires us to determine to which PCL category the text belongs (multi-label classification).Our system is based on the bidirectional encoder representations from transformers (BERT) model.For the single-label classification, our system applies a BertForSequence-Classification model to classify the input text.For the multi-label classification, we use the fine-tuned BERT model to extract the sentiment score of the text and a fully connected layer to classify the text into the PCL categories.Our system achieved relatively good results on the competition's official leaderboard.
LISACTeam at SemEval-2022 Task 6: A Transformer based Approach for Intended Sarcasm Detection in English Tweets

In this paper, we present our system and findings for SemEval-2022 Task 6 -iSarcasmEval: Intended Sarcasm Detection in English.The main objective of this task was to identify sarcastic tweets.This task was challenging mainly due to (1) the small training dataset that contains only 3468 tweets and (2) the imbalanced class distribution (25% sarcastic and 75% non-sarcastic).Our submitted model (ranked eighth on Sub-Task A and fifth on Sub-Task C) consists of a Transformer-based approach (BERTweet model).
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering

Multi-hop question answering (QA) combines multiple pieces of evidence to search for the correct answer.Reasoning over a text corpus (TextQA) and/or a knowledge base (KBQA) has been extensively studied and led to distinct system architectures.However, knowledge transfer between such two QA systems has been under-explored.Research questions like what knowledge is transferred or whether the transferred knowledge can help answer over one source using another one, are yet to be answered.In this paper, therefore, we study the knowledge transfer of multi-hop reasoning between structured and unstructured sources.We first propose a unified QA framework named SIMULTQA to enable knowledge transfer and bridge the distinct supervisions from KB and text sources.Then, we conduct extensive analyses to explore how knowledge is transferred by leveraging the pre-training and fine-tuning paradigm.We focus on the low-resource finetuning to show that pre-training SIMULTQA on one source can substantially improve its performance on the other source.More fine-grained analyses on transfer behaviors reveal the types of transferred knowledge and transfer patterns.We conclude with insights into how to construct better QA datasets and systems to exploit knowledge transfer for future work.
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance

This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity.The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles.To address this challenge, we evaluated the Word Mover's Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover's Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT.To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system.All our models achieve stable results in multilingual similarity estimation without learning parameters.
A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation

Recent natural language understanding (NLU) research on the Korean language has been vigorously maturing with the advancements of pretrained language models and datasets.However, Korean pretrained language models still struggle to generate a short sentence with a given condition based on compositionality and commonsense reasoning (i.e., generative commonsense reasoning).The two major challenges are inadequate data resources to develop generative commonsense reasoning regarding Korean linguistic features and to evaluate language models which are necessary for natural language generation (NLG).To solve these problems, we propose a textgeneration dataset for Korean generative commonsense reasoning and language model evaluation.In this work, a semi-automatic dataset construction approach filters out contents inexplicable to commonsense, ascertains quality, and reduces the cost of building the dataset.We also present an in-depth analysis of the generation results of language models with various evaluation metrics along with human-annotated scores.
System Description on Automatic Simultaneous Translation Workshop

This paper describes our system submitted on the third automatic simultaneous translation workshop at NAACL2022.We participate in the Chinese audio→English text direction of Chinese-to-English translation.Our speech-totext system is a pipeline system, in which we resort to rhymological features for audio split, ASRT model for speech recoginition, STACL model for streaming text translation.To translate streaming text, we use wait-k policy trained to generate the target sentence concurrently with the source sentence, but always k words behind.We propose a competitive simultaneous translation system and rank 3rd in the audio input track.The code will release soon.
Retrieval Based Response Letter Generation For a Customer Care Setting

Letter-like communications (such as email) are a major means of customer relationship management within customer-facing organizations.These communications are initiated on a channel by requests from customers and then responded to by the organization on the same channel.For decades, the job has almost entirely been conducted by human agents who attempt to provide the most appropriate reaction to the request.Rules have been made to standardize the overall customer service process and make sure the customers receive professional responses.Recent progress in natural language processing has made it possible to automate response generation.However, the diversity and open nature of customer queries and the lack of structured knowledge bases make this task even more challenging than typical task-oriented language generation tasks.Keeping those obstacles in mind, we propose a deep-learning based response letter generation framework that attempts to retrieve knowledge from historical responses and utilize it to generate an appropriate reply.Our model uses data augmentation to address the insufficiency of query-response pairs and employs a ranking mechanism to choose the best response from multiple potential options.We show that our technique outperforms the baselines by significant margins while producing consistent and informative responses.