A Self-Supervised Automatic Post-Editing Data Generation Tool

Data building for automatic post-editing (APE) requires extensive and expert-level human effort, as it contains an elaborate process that involves identifying errors in sentences and providing suitable revisions. Hence, we develop a self-supervised data generation tool, deployable as a web application, that minimizes human supervision and constructs personalized APE data from a parallel corpus for several language pairs with English as the target language. Data-centric APE research can be conducted using this tool, involving many language pairs that have not been studied thus far owing to the lack of suitable data.Comment: Accepted for DataPerf workshop at ICML 202

QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation

With the recent advance in neural machine translation demonstrating its importance, research on quality estimation (QE) has been steadily progressing. QE aims to automatically predict the quality of machine translation (MT) output without reference sentences. Despite its high utility in the real world, there remain several limitations concerning manual QE data creation: inevitably incurred non-trivial costs due to the need for translation experts, and issues with data scaling and language expansion. To tackle these limitations, we present QUAK, a Korean-English synthetic QE dataset generated in a fully automatic manner. This consists of three sub-QUAK datasets QUAK-M, QUAK-P, and QUAK-H, produced through three strategies that are relatively free from language constraints. Since each strategy requires no human effort, which facilitates scalability, we scale our data up to 1.58M for QUAK-P, H and 6.58M for QUAK-M. As an experiment, we quantitatively analyze word-level QE results in various ways while performing statistical analysis. Moreover, we show that datasets scaled in an efficient way also contribute to performance improvements by observing meaningful performance gains in QUAK-M, P when adding data up to 1.58M

Ice Velocity Mapping of Ross Ice Shelf, Antarctica by Matching Surface Undulations Measured by Icesat Laser Altimetry

We present a novel method for estimating the surface horizontal velocity on ice shelves using laser altimetrydata from the Ice Cloud and land Elevation Satellite (ICESat; 20032009). The method matches undulations measured at crossover points between successive campaigns

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Dense-to-Question and Sparse-to-Answer: Hybrid Retriever System for Industrial Frequently Asked Questions

The term “Frequently asked questions” (FAQ) refers to a query that is asked repeatedly and produces a manually constructed response. It is one of the most important factors influencing customer repurchase and brand loyalty; thus, most industry domains invest heavily in it. This has led to deep-learning-based retrieval models being studied. However, training a model and creating a database specializing in each industry domain comes at a high cost, especially when using a chatbot-based conversation system, as a large amount of resources must be continuously input for the FAQ system’s maintenance. It is also difficult for small- and medium-sized companies and national institutions to build individualized training data and databases and obtain satisfactory results. As a result, based on the deep learning information retrieval module, we propose a method of returning responses to customer inquiries using only data that can be easily obtained from companies. We hybridize dense embedding and sparse embedding in this work to make it more robust in professional terms, and we propose new functions to adjust the weight ratio and scale the results returned by the two modules

Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation

Quality estimation (QE) has recently gained increasing interest as it can predict the quality of machine translation results without a reference translation. QE is an annual shared task at the Conference on Machine Translation (WMT), and most recent studies have applied the multilingual pretrained language model (mPLM) to address this task. Recent studies have focused on the performance improvement of this task using data augmentation with finetuning based on a large-scale mPLM. In this study, we eliminate the effects of data augmentation and conduct a pure performance comparison between various mPLMs. Separate from the recent performance-driven QE research involved in competitions addressing a shared task, we utilize the comparison for sub-tasks from WMT20 and identify an optimal mPLM. Moreover, we demonstrate QE using the multilingual BART model, which has not yet been utilized, and conduct comparative experiments and analyses with cross-lingual language models (XLMs), multilingual BERT, and XLM-RoBERTa

The ASR Post-Processor Performance Challenges of BackTranScription (BTS): Data-Centric and Model-Centric Approaches

Training an automatic speech recognition (ASR) post-processor based on sequence-to-sequence (S2S) requires a parallel pair (e.g., speech recognition result and human post-edited sentence) to construct the dataset, which demands a great amount of human labor. BackTransScription (BTS) proposes a data-building method to mitigate the limitations of the existing S2S based ASR post-processors, which can automatically generate vast amounts of training datasets, reducing time and cost in data construction. Despite the emergence of this novel approach, the BTS-based ASR post-processor still has research challenges and is mostly untested in diverse approaches. In this study, we highlight these challenges through detailed experiments by analyzing the data-centric approach (i.e., controlling the amount of data without model alteration) and the model-centric approach (i.e., model modification). In other words, we attempt to point out problems with the current trend of research pursuing a model-centric approach and alert against ignoring the importance of the data. Our experiment results show that the data-centric approach outperformed the model-centric approach by +11.69, +17.64, and +19.02 in the F1-score, BLEU, and GLEU tests

BERTOEIC: Solving TOEIC Problems Using Simple and Efficient Data Augmentation Techniques with Pretrained Transformer Encoders

Recent studies have attempted to understand natural language and infer answers. Machine reading comprehension is one of the representatives, and several related datasets have been opened. However, there are few official open datasets for the Test of English for International Communication (TOEIC), which is widely used for evaluating people’s English proficiency, and research for further advancement is not being actively conducted. We consider that the reason why deep learning research for TOEIC is difficult is due to the data scarcity problem, so we therefore propose two data augmentation methods to improve the model in a low resource environment. Considering the attributes of the semantic and grammar problem type in TOEIC, the proposed methods can augment the data similar to the real TOEIC problem by using POS-tagging and Lemmatizing. In addition, we confirmed the importance of understanding semantics and grammar in TOEIC through experiments on each proposed methodology and experiments according to the amount of data. The proposed methods address the data shortage problem of TOEIC and enable an acceptable human-level performance

Uncovering the Risks and Drawbacks Associated With the Use of Synthetic Data for Grammatical Error Correction

In a Data-Centric AI paradigm, the model performance is enhanced without altering the model architecture, as evidenced by real-world and benchmark dataset demonstrations. With the advancements of large language models (LLM), it has become increasingly feasible to generate high-quality synthetic data, while considering the need to construct fully synthetic datasets for real-world data containing numerous personal information. However, in-depth validation of the solely synthetic data setting has yet to be conducted, despite the increased possibility of models trained exclusively on fully synthetic data emerging in the future. Therefore, we examined the question, “Do data quality control techniques (known to positively impact data-centric AI) consistently aid models trained exclusively on synthetic datasets?”. To explore this query, we performed detailed analyses using synthetic datasets generated for speech recognition postprocessing using the BackTranScription (BTS) approach. Our study primarily addressed the potential adverse effects of data quality control measures (e.g., noise injection and balanced data) and training strategies in the context of synthetic-only experiments. As a result of the experiment, we observed the negative effect that the data-centric methodology drops by a maximum of 44.03 points in the fully synthetic data setting

Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC

The machine translation system aims to translate source language into target language. Recent studies on MT systems mainly focus on neural machine translation. One factor that significantly affects the performance of NMT is the availability of high-quality parallel corpora. However, high-quality parallel corpora concerning Korean are relatively scarce compared to those associated with other high-resource languages, such as German or Italian. To address this problem, AI Hub recently released seven types of parallel corpora for Korean. In this study, we conduct an in-depth verification of the quality of corresponding parallel corpora through Linguistic Inquiry and Word Count (LIWC) and several relevant experiments. LIWC is a word-counting software program that can analyze corpora in multiple ways and extract linguistic features as a dictionary base. To the best of our knowledge, this study is the first to use LIWC to analyze parallel corpora in the field of NMT. Our findings suggest the direction of further research toward obtaining the improved quality parallel corpora through our correlation analysis in LIWC and NMT performance