Reviewerly/exHarmony huggingface dataset:
Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar

Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model.With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG).CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination.We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from context-free phrase structure.Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions.This evidence for CCG from neuroimaging bolsters the more general case for mildly context-sensitive grammars in the cognitive science of language.
Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation

Successful methods for unsupervised neural machine translation (UNMT) employ crosslingual pretraining via self-supervision, often in the form of a masked language modeling or a sequence generation task, which requires the model to align the lexical-and high-level representations of the two languages.While cross-lingual pretraining works for similar languages with abundant corpora, it performs poorly in low-resource and distant languages.Previous research has shown that this is because the representations are not sufficiently aligned.In this paper, we enhance the bilingual masked language model pretraining with lexical-level information by using type-level cross-lingual subword embeddings.Empirical results demonstrate improved performance both on UNMT (up to 4.5 BLEU) and bilingual lexicon induction using our method compared to a UNMT baseline.
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing

We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs.More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English).Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well.Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance.In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data.We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions.Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU).Our models place first in the overall ranking of the English-Hinglish official shared task.
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity

It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the sparse model inference via weight pruning, i.e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy. This paper designs a novel mobile inference acceleration framework GRIM that is General to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and that achieves Real-time execution and high accuracy, leveraging fine-grained structured sparse model Inference and compiler optimizations for Mobiles. We start by proposing a new fine-grained structured sparsity scheme through the Block-based Column-Row (BCR) pruning. Based on this new fine-grained structured sparsity, our GRIM framework consists of two parts: (a) the compiler optimization and code generation for real-time mobile inference; and (b) the BCR pruning optimizations for determining pruning hyperparameters and performing weight pruning. We compare GRIM with Alibaba MNN, TVM, TensorFlow-Lite, a sparse implementation based on CSR, PatDNN, and ESE (a representative FPGA inference acceleration framework for RNNs), and achieve up to 14.08× speedup.
A BERT-based transfer learning approach to text classification on software requirements specifications

In a software development life cycle, software requirements specifications (SRS) written in an incomprehensible language might hinder the success of the project in later stages.In such cases, the subjective and ambiguous nature of the natural languages can be considered as a cause for the failure of the final product.Redundancy and/or controversial information in the SRS documents might also result in additional costs and time loss, reducing the overall efficiency of the project.With the recent advances in machine learning, there is an increased effort to develop automated solutions for a seamless SRS design.However, most vanilla machine learning approaches ignore the semantics of the software artifacts or integrating domain-specific knowledge into the underlying natural language processing tasks, and therefore tend to generate inaccurate results.With such concerns in mind, we consider a transfer learning approach in our study, which is based on an existing pre-trained language model called DistilBERT.We specifically examine the DistilBERT's ability in multi-class text classification on SRS data using various finetuning methods, and compare its performance with other deep learning methods such as LSTM and BiLSTM.We test the performance of these models using two datasets: DOORS Next Generation dataset and PROMISE-NFR dataset.Our numerical results demonstrate that DistilBERT perform well for various text classification tasks over the SRS datasets and shows significant promise to be used for automating the software development processes.
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots

Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former (PolyGloss) uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter (Bumblebee) directly aligns the clean example with its translations before extracting phrases as perturbations. Bumblebee has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme, Code-mixed Adversarial Training (CAT), that trains in the same number of steps as the original model. Even after controlling for the extra training data introduced, CAT improves model accuracy when the model is prevented from relying on lexical overlaps (+3.45), with a negligible drop (-0.15 points) in performance on the original XNLI test set. t-SNE visualizations reveal that CAT improves a model’s language agnosticity. This paper will be published in the proceedings of NAACL-HLT 2021.
Open Data Policies among Library and Information Science Journals

Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record. The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies. This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors. Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies.
On the Impact of Random Seeds on the Fairness of Clinical Classifiers

Silvio Amir, Jan-Willem van de Meent, Byron Wallace. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
FanfictionNLP: A Text Processing Pipeline for Fanfiction

Fanfiction presents an opportunity as a data source for research in NLP, education, and social science.However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction.We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters.The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters.Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes.For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories.This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution.
Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation

For a computer to naturally interact with a human, it needs to be human-like.In this paper, we propose a neural response generation model with multi-task learning of generation and classification, focusing on emotion.Our model based on BART (Lewis et al., 2020), a pre-trained transformer encoderdecoder model, is trained to generate responses and recognize emotions simultaneously.Furthermore, we weight the losses for the tasks to control the update of parameters.Automatic evaluations and crowdsourced manual evaluations show that the proposed model makes generated responses more emotionally aware.
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrasestructure grammar induction.However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols.In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols.We further use neural parameterization for the new form to improve unsupervised parsing performance.We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols.
Smart Autonomous Wheelchair Controlled by Voice Commands-Aided by Tracking System

This study focuses on the design of an autonomous wheelchair based smart driving features for disabled persons. The movement directions and position tracking of the wheelchair are controlled and localized by pre-defined voice commands and global positioning system (GPS), respectively. Arduino microcontroller based on speaker dependent voice recognition module and tracking system based on quad-board SIM808 has been used to help the wheelchair navigation. The experimental tests of the proposed system have been done and given satisfactory results in controlling the wheelchair and making a call on demand. Hence the proposed system is a simple, easy to use and lowcost hardware for designing.
Film Directing for Computer Games and Animation

Abstract Over the last forty years, researchers in computer graphics have proposed a large variety of theoretical models and computer implementations of a virtual film director, capable of creating movies from minimal input such as a screenplay or storyboard. The underlying film directing techniques are also in high demand to assist and automate the generation of movies in computer games and animation. The goal of this survey is to characterize the spectrum of applications that require film directing, to present a historical and up‐to‐date summary of research in algorithmic film directing, and to identify promising avenues and hot topics for future research.
Multi-Style Transfer with Discriminative Feedback on Disjoint Corpus

Style transfer has been widely explored in natural language generation with non-parallel corpus by directly or indirectly extracting a notion of style from source and target domain corpus.A common shortcoming of existing approaches is the prerequisite of joint annotations across all the stylistic dimensions under consideration.Availability of such dataset across a combination of styles limits the extension of these setups to multiple style dimensions.While cascading single-dimensional models across multiple styles is a possibility, it suffers from content loss, especially when the style dimensions are not completely independent of each other.In our work, we relax this requirement of jointly annotated data across multiple styles by using independently acquired data across different style dimensions without any additional annotations.We initialize an encoder-decoder setup with transformerbased language model pre-trained on a generic corpus and enhance its re-writing capability to multiple target style dimensions by employing multiple style-aware language models as discriminators.Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style dimensions while preserving content of the input text.We compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.
The Hybrid of Jaro-Winkler and Rabin-Karp Algorithm in Detecting Indonesian Text Similarity

The String-matching technique is part of the similarity technique. This technique can detect the similarity level of the text. The Rabin-Karp is an algorithm of string-matching type. The Rabin-Karp is capable of multiple patterns searching but does not match a single pattern. The Jaro-Winkler Distance algorithm can find strings within approximate string matching. This algorithm is very suitable and gives the best results on the matching of two short strings. This study aims to overcome the shortcomings of the Rabin-Karp algorithm in the single pattern search process by combining the Jaro-Winkler and Rabin-Karp algorithm methods. The merging process started from pre-processing and forming the K-Gram data. Then, it was followed by the calculation of the hash value for each K-Gram by the Rabin-Karp algorithm. The process of finding the same hash score and calculating the percentage level of data similarity used the Jaro-Winkler algorithm. The test was done by comparing words, sentences, and journal abstracts that have been rearranged. The average percentage of the test results for the similarity level of words in the combination algorithm has increased. In contrast, the results of the percentage test for the level of similarity of sentences and journal abstracts have decreased. The experimental results showed that the combination of the Jaro-Winkler algorithm on the Rabin-Karp algorithm can improve the similarity of text accuracy.
A Simple and Efficient Multi-Task Learning Approach for Conditioned Dialogue Generation

Conditioned dialogue generation suffers from the scarcity of labeled responses.In this work, we exploit labeled non-dialogue text data related to the condition, which are much easier to collect.We propose a multi-task learning approach to leverage both labeled dialogue and text data.The 3 tasks jointly optimize the same pre-trained Transformer -conditioned dialogue generation task on the labeled dialogue data, conditioned language encoding task and conditioned language generation task on the labeled text data.Experimental results show that our approach outperforms the stateof-the-art models by leveraging the labeled texts, and it also obtains larger improvement in performance comparing to the previous methods to leverage text data.
damo_nlp at MEDIQA 2021: Knowledge-based Preprocessing and Coverage-oriented Reranking for Medical Question Summarization

Medical question summarization is an important but difficult task, where the input is often complex and erroneous while annotated data is expensive to acquire.We report our participation in the MEDIQA 2021 question summarization task in which we are required to address these challenges.We start from pre-trained conditional generative language models, use knowledge bases to help correct input errors, and rerank single system outputs to boost coverage.Experimental results show significant improvement in stringbased metrics.Hi, Please can you help -I am writing from South Africa.My daughter suffers with acute folliculitus, and has been since the age of 13.She is now 20 and is in so much distress as nothing seems to alleviate the itching and soreness...I am writing to you for any help you could give me to try and assist her.Could you recommend a specialist and someone who could help us with research?Please could you point us in the right direction
(Quantum) Collision Attacks on Reduced Simpira v2

Simpira v2 is an AES-based permutation proposed by Gueron and Mouha at ASIACRYPT 2016. In this paper, we build an improved MILP model to count the differential and linear active Sboxes for Simpira v2, which achieves tighter bounds of the minimum number of active Sboxes for a few versions of Simpira v2. Then, based on the new model, we find some new truncated differentials for Simpira v2 and give a series (quantum) collision attacks on two versions of reduced Simpira v2.
Multi-task Learning of Negation and Speculation for Targeted Sentiment Classification

The majority of work in targeted sentiment analysis has concentrated on finding better methods to improve the overall results.Within this paper we show that these models are not robust to linguistic phenomena, specifically negation and speculation.In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena.Further we create two challenge datasets to evaluate model performance on negated and speculative samples.We find that multi-task models and transfer learning via language modelling can improve performance on these challenge datasets, but the overall performances indicate that there is still much room for improvement.We release both the datasets and the source code at https://github.com/jerbarnes/multitask_negation_ for_targeted_sentiment.
Enhancing Multiple-Choice Question Answering with Causal Knowledge

The task of causal question answering aims to reason about causes and effects over a provided real or hypothetical premise.Recent approaches have converged on using transformerbased language models to solve question answering tasks.However, pretrained language models often struggle when external knowledge is not present in the premise or when additional context is required to answer the question.To the best of our knowledge, no prior work has explored the efficacy of augmenting pretrained language models with external causal knowledge for multiple-choice causal question answering.In this paper, we present novel strategies for the representation of causal knowledge.Our empirical results demonstrate the efficacy of augmenting pretrained models with external causal knowledge.We show improved performance on the COPA (Choice of Plausible Alternatives) and WIQA (What If Reasoning Over Procedural Text) benchmark tasks.On the WIQA benchmark, our approach is competitive with the state-of-the-art and exceeds it within the evaluation subcategories of In-Paragraph and Out-of-Paragraph perturbations.
Bridging Resolution: Making Sense of the State of the Art

While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rulebased approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?)and ( 2) what should be improved.To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products

Predicting the answer to a product-related question is an emerging field of research that recently attracted a lot of attention.Answering subjective and opinion-based questions is most challenging due to the dependency on customer-generated content.Previous works mostly focused on review-aware answer prediction; however, these approaches fail for new or unpopular products, having no (or only a few) reviews at hand.In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products.We measure the contextual similarity between products based on the answers they provide for the same question.A mixture-of-expert framework is used to predict the answer by aggregating the answers from contextually similar products.Empirical results demonstrate that our model outperforms strong baselines on some segments of questions, namely those that have roughly ten or more similar resolved questions in the corpus.We additionally publish two large-scale datasets 1 used in this work, one is of similar product question pairs, and the second is of product question-answer pairs.
Go-Ethereum for electronic voting system using clique as proof-of-authority

Current advances in information technology have brought about significant changes, including ways to carry out elections using computer technology known as e-voting. Blockchain underlies the popularity of the digital currency Bitcoin and some other digital money, sparking the start of a new era of Internet use, including electronic voting (e-voting) system. In this work, we proposed designing and implementing an evoting system powered by the Ethereum blockchain. We used real-world data from the Indonesian Election Commission (KPU) with 26 candidates and 15,725 voters from the Jelupang sub-district. The testing and evaluation results using three miners in the blockchain show that the system could commit around 23 transactions per second. All 15,725 votes are committed to the blockchain successfully within 12.75 minutes. The total time required for creating all blockchain accounts is 13.4 hours.