pwc-archive/papers-with-abstracts huggingface dataset:
Clustering of Spell Variations for Proper Nouns Transliterated from the other languages
Authors: Prathamesh Pawar
Date: 2023-10-12 00:00:00
Tasks: Clustering, Proper Noun

One of the prominent problems with processing and operating on text data is the non uniformity of it. Due to the change in the dialects and languages, the caliber of translation is low. This creates a unique problem while using NLP in text data; which is the spell variation arising from the inconsistent translations and transliterations. This problem can also be further aggravated by the human error arising from the various ways to write a Proper Noun from an Indian language into its English equivalent. Translating proper nouns originating from Indian languages can be complicated as some proper nouns are also used as common nouns which might be taken literally. Applications of NLP that require addresses, names and other proper nouns face this problem frequently. We propose a method to cluster these spell variations for proper nouns using ML techniques and mathematical similarity equations. We aimed to use Affinity Propagation to determine relative similarity between the tokens. The results are augmented by filtering the token-variation pair by a similarity threshold. We were able to reduce the spell variations by a considerable amount. This application can significantly reduce the amount of human annotation efforts needed for data cleansing and formatting.
Crosslingual Structural Priming and the Pre-Training Dynamics of Bilingual Language Models
Authors: Catherine Arnett, Tyler A. Chang, James A. Michaelov, Benjamin K. Bergen
Date: 2023-10-11 00:00:00
Tasks: Language Modeling, Language Modelling

Do multilingual language models share abstract grammatical representations across languages, and if so, when do these develop? Following Sinclair et al. (2022), we use structural priming to test for abstract grammatical representations with causal effects on model outputs. We extend the approach to a Dutch-English bilingual setting, and we evaluate a Dutch-English language model during pre-training. We find that crosslingual structural priming effects emerge early after exposure to the second language, with less than 1M tokens of data in that language. We discuss implications for data contamination, low-resource transfer, and how abstract grammatical representations emerge in multilingual models.
Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention
Authors: Huiyin Xue, Nikolaos Aletras
Date: 2023-10-11 00:00:00

Scaling pre-trained language models has resulted in large performance gains in various natural language processing tasks but comes with a large cost in memory requirements. Inspired by the position embeddings in transformers, we aim to simplify and reduce the memory footprint of the multi-head attention (MHA) mechanism. We propose an alternative module that uses only a single shared projection matrix and multiple head embeddings (MHE), i.e. one per head. We empirically demonstrate that our MHE attention is substantially more memory efficient compared to alternative attention mechanisms while achieving high predictive performance retention ratio to vanilla MHA on several downstream tasks. MHE attention only requires a negligible fraction of additional parameters ($3nd$, where $n$ is the number of attention heads and $d$ the size of the head embeddings) compared to a single-head attention, while MHA requires $(3n^2-3n)d^2-3nd$ additional parameters.
Assessing Evaluation Metrics for Neural Test Oracle Generation
Authors: Jiho Shin, Hadi Hemmati, Moshi Wei, Song Wang
Date: 2023-10-11 00:00:00

In this work, we revisit existing oracle generation studies plus ChatGPT to empirically investigate the current standing of their performance in both NLG-based and test adequacy metrics. Specifically, we train and run four state-of-the-art test oracle generation models on five NLG-based and two test adequacy metrics for our analysis. We apply two different correlation analyses between these two different sets of metrics. Surprisingly, we found no significant correlation between the NLG-based metrics and test adequacy metrics. For instance, oracles generated from ChatGPT on the project activemq-artemis had the highest performance on all the NLG-based metrics among the studied NOGs, however, it had the most number of projects with a decrease in test adequacy metrics compared to all the studied NOGs. We further conduct a qualitative analysis to explore the reasons behind our observations, we found that oracles with high NLG-based metrics but low test adequacy metrics tend to have complex or multiple chained method invocations within the oracle's parameters, making it hard for the model to generate completely, affecting the test adequacy metrics. On the other hand, oracles with low NLG-based metrics but high test adequacy metrics tend to have to call different assertion types or a different method that functions similarly to the ones in the ground truth. Overall, this work complements prior studies on test oracle generation with an extensive performance evaluation with both NLG and test adequacy metrics and provides guidelines for better assessment of deep learning applications in software test generation in the future.
Framework for Question-Answering in Sanskrit through Automated Construction of Knowledge Graphs
Authors: Hrishikesh Terdalkar, Arnab Bhattacharya
Date: 2023-10-11 00:00:00
Proceeding: WS 2019 10
Tasks: Knowledge Graphs, Question Answering

Sanskrit (sa\d{m}sk\d{r}ta) enjoys one of the largest and most varied literature in the whole world. Extracting the knowledge from it, however, is a challenging task due to multiple reasons including complexity of the language and paucity of standard natural language processing tools. In this paper, we target the problem of building knowledge graphs for particular types of relationships from sa\d{m}sk\d{r}ta texts. We build a natural language question-answering system in sa\d{m}sk\d{r}ta that uses the knowledge graph to answer factoid questions. We design a framework for the overall system and implement two separate instances of the system on human relationships from mah\=abh\=arata and r\=am\=aya\d{n}a, and one instance on synonymous relationships from bh\=avaprak\=a\'sa nigha\d{n}\d{t}u, a technical text from \=ayurveda. We show that about 50% of the factoid questions can be answered correctly by the system. More importantly, we analyse the shortcomings of the system in detail for each step, and discuss the possible ways forward.
Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation
Authors: Hrishikesh Terdalkar, Arnab Bhattacharya
Date: 2023-10-11 00:00:00
Tasks: Boundary Detection, Sentence

One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of NLP tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://github.com/Antarlekhaka/code.
Non-autoregressive Text Editing with Copy-aware Latent Alignments
Authors: Yu Zhang, Yue Zhang, Leyang Cui, Guohong Fu
Date: 2023-10-11 00:00:00
Tasks: Management, Sentence, Sentence Fusion

Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in generalizing to other languages. In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments. We make a crucial extension to CTC by introducing the copy operation into the edit space, thus enabling more efficient management of textual overlap in editing. We conduct extensive experiments on GEC and sentence fusion tasks, showing that our proposed method significantly outperforms existing Seq2Edit models and achieves similar or even better results than Seq2Seq with over $4\times$ speedup. Moreover, it demonstrates good generalizability on German and Russian. In-depth analyses reveal the strengths of our method in terms of the robustness under various scenarios and generating fluent and flexible outputs.
Faithfulness Measurable Masked Language Models
Authors: Andreas Madsen, Siva Reddy, Sarath Chandar
Date: 2023-10-11 00:00:00

A common approach to explaining NLP models is to use importance measures that express which tokens are important for a prediction. Unfortunately, such explanations are often wrong despite being persuasive. Therefore, it is essential to measure their faithfulness. One such metric is if tokens are truly important, then masking them should result in worse model performance. However, token masking introduces out-of-distribution issues, and existing solutions that address this are computationally expensive and employ proxy models. Furthermore, other metrics are very limited in scope. This work proposes an inherently faithfulness measurable model that addresses these challenges. This is achieved using a novel fine-tuning method that incorporates masking, such that masking tokens become in-distribution by design. This differs from existing approaches, which are completely model-agnostic but are inapplicable in practice. We demonstrate the generality of our approach by applying it to 16 different datasets and validate it using statistical in-distribution tests. The faithfulness is then measured with 9 different importance measures. Because masking is in-distribution, importance measures that themselves use masking become consistently more faithful. Additionally, because the model makes faithfulness cheap to measure, we can optimize explanations towards maximal faithfulness; thus, our model becomes indirectly inherently explainable.
Language Models As Semantic Indexers
Authors: Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang
Date: 2023-10-11 00:00:00
Tasks: Contrastive Learning, Information Retrieval, Language Modelling, Retrieval

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deriving IDs based on the embeddings. However, each step introduces potential information loss, and there is usually an inherent mismatch between the distribution of embeddings within the latent space produced by text encoders and the anticipated distribution required for semantic indexing. It is non-trivial to design a method that can learn the document's semantic representations and its hierarchical structure simultaneously, given that semantic IDs are discrete and sequentially structured, and the semantic supervision is deficient. In this paper, we introduce LMIndexer, a self-supervised framework to learn semantic IDs with a generative language model. We tackle the challenge of sequential discrete ID by introducing a semantic indexer capable of generating neural sequential discrete representations with progressive training and contrastive learning. In response to the semantic supervision deficiency, we propose to train the model with a self-supervised document reconstruction objective. We show the high quality of the learned IDs and demonstrate their effectiveness on three tasks including recommendation, product search, and document retrieval on five datasets from various domains. Code is available at https://github.com/PeterGriffinJin/LMIndexer.
Ontology Enrichment for Effective Fine-grained Entity Typing
Authors: Siru Ouyang, Jiaxin Huang, Pranav Pillai, Yunyi Zhang, Yu Zhang, Jiawei Han
Date: 2023-10-11 00:00:00
Tasks: Entity Typing

Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional methods for FET require extensive human annotation, which is time-consuming and costly. Recent studies have been developing weakly supervised or zero-shot approaches. We study the setting of zero-shot FET where only an ontology is provided. However, most existing ontology structures lack rich supporting information and even contain ambiguous relations, making them ineffective in guiding FET. Recently developed language models, though promising in various few-shot and zero-shot NLP tasks, may face challenges in zero-shot FET due to their lack of interaction with task-specific ontology. In this study, we propose OnEFET, where we (1) enrich each node in the ontology structure with two types of extra information: instance information for training sample augmentation and topic information to relate types to contexts, and (2) develop a coarse-to-fine typing algorithm that exploits the enriched information by training an entailment model with contrasting topics and instance-based augmented training samples. Our experiments show that OnEFET achieves high-quality fine-grained entity typing without human annotation, outperforming existing zero-shot methods by a large margin and rivaling supervised methods.
Towards Running Time Analysis of Interactive Multi-objective Evolutionary Algorithms
Authors: Tianhao Lu, Chao Bian, Chao Qian
Date: 2023-10-12 00:00:00
Tasks: Decision Making, Evolutionary Algorithms

Evolutionary algorithms (EAs) are widely used for multi-objective optimization due to their population-based nature. Traditional multi-objective EAs (MOEAs) generate a large set of solutions to approximate the Pareto front, leaving a decision maker (DM) with the task of selecting a preferred solution. However, this process can be inefficient and time-consuming, especially when there are many objectives or the subjective preferences of DM is known. To address this issue, interactive MOEAs (iMOEAs) combine decision making into the optimization process, i.e., update the population with the help of the DM. In contrast to their wide applications, there has existed only two pieces of theoretical works on iMOEAs, which only considered interactive variants of the two simple single-objective algorithms, RLS and (1+1)-EA. This paper provides the first running time analysis (the essential theoretical aspect of EAs) for practical iMOEAs. Specifically, we prove that the expected running time of the well-developed interactive NSGA-II (called R-NSGA-II) for solving the OneMinMax and OneJumpZeroJump problems is $O(n \log n)$ and $O(n^k)$, respectively, which are all asymptotically faster than the traditional NSGA-II. Meanwhile, we present a variant of OneMinMax, and prove that R-NSGA-II can be exponentially slower than NSGA-II. These results provide theoretical justification for the effectiveness of iMOEAs while identifying situations where they may fail. Experiments are also conducted to validate the theoretical results.
A Convolutional Network Adaptation for Cortical Classification During Mobile Brain Imaging
Authors: Benjamin Cichy, Jamie Lukos, Mohammad Alam, J. Cortney Bradford, Nicholas Wymbs
Date: 2023-10-11 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, ERP

Deep neural networks (DNN) have become increasingly utilized in brain-computer interface (BCI) technologies with the outset goal of classifying human physiological signals in computer-readable format. While our present understanding of DNN usage for BCI is promising, we have little experience in deciphering neural events from dynamic freely-mobile situations. Using an improved version of EEGNet, our goal was to classify cognitive events from electroencephalography (EEG) signals while subjects simultaneously walked on a treadmill, sometimes while carrying a rucksack equivalent to 40% of their body weight. Walking subjects simultaneously performed a visual oddball target detection task, eliciting the P300 event-related potential (ERP), which then served as the DNN classification target. We found the base EEGNet to reach classification levels well above chance, with similar performance to previously reported P300 results. We found performance to be robust to noise, with classification similar for walking and loaded walking, with respect to standard seated condition with minimal movement. With additional architectural search and tuning to the EEGNet model (termed Cog-Neuro, herein; CN-EEGNet), we reached classification accuracy of greater than 95%, similar to previously reported state of the art levels in seated P300 tasks. To our knowledge, these results are the first documented implementation of a DNN for the classification of cognitive neural state during dual-task walking. The classification of one's ongoing cognitive state during a demanding physical task establishes the utility for BCI in complex environments.
An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks
Authors: Beyza Zeynep Ucpinar, Mustafa Altay Karamuftuoglu, Sasan Razmkhah, Massoud Pedram
Date: 2023-10-11 00:00:00

We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The thresholds of neurons can be increased or decreased depending on the desired application-specific spike generation rate. This mechanism provides us with a flexible design and scalable circuit structure. We demonstrate the trainable neuron structure under different operating scenarios. The circuits are designed and optimized for the MIT LL SFQ5ee fabrication process. Margin values for all parameters are above 25\% with a 3GHz throughput for a 16-input neuron.
Personalised dynamic super learning: an application in predicting hemodiafiltration convection volumes
Authors: Arthur Chatton, Michèle Bally, Renée Lévesque, Ivana Malenica, Robert W. Platt, Mireille E. Schnitzer
Date: 2023-10-12 00:00:00

Obtaining continuously updated predictions is a major challenge for personalised medicine. Leveraging combinations of parametric regressions and machine learning approaches, the personalised online super learner (POSL) can achieve such dynamic and personalised predictions. We adapt POSL to predict a repeated continuous outcome dynamically and propose a new way to validate such personalised or dynamic prediction models. We illustrate its performance by predicting the convection volume of patients undergoing hemodiafiltration. POSL outperformed its candidate learners with respect to median absolute error, calibration-in-the-large, discrimination, and net benefit. We finally discuss the choices and challenges underlying the use of POSL.
Extensions of Heterogeneity in Integration and Prediction (HIP) with R Shiny Application
Authors: J. Butts, C. Wendt, R. Bowler, C. P. Hersh, Q. Long, L. Eberly, S. E. Safo
Date: 2023-10-12 00:00:00

Multiple data views measured on the same set of participants is becoming more common and has the potential to deepen our understanding of many complex diseases by analyzing these different views simultaneously. Equally important, many of these complex diseases show evidence of subgroup heterogeneity (e.g., by sex or race). HIP (Heterogeneity in Integration and Prediction) is among the first methods proposed to integrate multiple data views while also accounting for subgroup heterogeneity to identify common and subgroup-specific markers of a particular disease. However, HIP is applicable to continuous outcomes and requires programming expertise by the user. Here we propose extensions to HIP that accommodate multi-class, Poisson, and Zero-Inflated Poisson outcomes while retaining the benefits of HIP. Additionally, we introduce an R Shiny application, accessible on shinyapps.io at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/, that provides an interface with the Python implementation of HIP to allow more researchers to use the method anywhere and on any device. We applied HIP to identify genes and proteins common and specific to males and females that are associated with exacerbation frequency. Although some of the identified genes and proteins show evidence of a relationship with chronic obstructive pulmonary disease (COPD) in existing literature, others may be candidates for future research investigating their relationship with COPD. We demonstrate the use of the Shiny application with a publicly available data. An R-package for HIP would be made available at https://github.com/lasandrall/HIP.
Evaluation of ChatGPT-Generated Medical Responses: A Systematic Review and Meta-Analysis
Authors: Qiuhong Wei, Zhengxiong Yao, Ying Cui, Bo Wei, Zhezhen Jin, Ximing Xu
Date: 2023-10-12 00:00:00
Tasks: Articles

Large language models such as ChatGPT are increasingly explored in medical domains. However, the absence of standard guidelines for performance evaluation has led to methodological inconsistencies. This study aims to summarize the available evidence on evaluating ChatGPT's performance in medicine and provide direction for future research. We searched ten medical literature databases on June 15, 2023, using the keyword "ChatGPT". A total of 3520 articles were identified, of which 60 were reviewed and summarized in this paper and 17 were included in the meta-analysis. The analysis showed that ChatGPT displayed an overall integrated accuracy of 56% (95% CI: 51%-60%, I2 = 87%) in addressing medical queries. However, the studies varied in question resource, question-asking process, and evaluation metrics. Moreover, many studies failed to report methodological details, including the version of ChatGPT and whether each question was used independently or repeatedly. Our findings revealed that although ChatGPT demonstrated considerable potential for application in healthcare, the heterogeneity of the studies and insufficient reporting may affect the reliability of these results. Further well-designed studies with comprehensive and transparent reporting are needed to evaluate ChatGPT's performance in medicine.