Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias

Machine Reading Comprehension (MRC) models tend to take advantage of spurious correlations (also known as dataset bias or annotation artifacts in the research community). Consequently, these models may perform the MRC task without fully comprehending the given context and question, which is undesirable since it may result in low robustness against distribution shift. This paper delves into the concept of answer-position bias, where a significant percentage of training questions have answers located solely in the first sentence of the context. We propose a Single-Sentence Reader as a new approach for addressing answer position bias in MRC. We implement this approach using six different models and thoroughly analyze their performance. Remarkably, our proposed Single-Sentence Readers achieve results that nearly match those of models trained on conventional training sets, proving their effectiveness. Our study also discusses several challenges our Single-Sentence Readers encounter and proposes a potential solution.Comment: 11 pages, 5 tables, 2 figure

Detecting Machine-Translated Text using Back Translation

Machine-translated text plays a crucial role in the communication of people using different languages. However, adversaries can use such text for malicious purposes such as plagiarism and fake review. The existing methods detected a machine-translated text only using the text's intrinsic content, but they are unsuitable for classifying the machine-translated and human-written texts with the same meanings. We have proposed a method to extract features used to distinguish machine/human text based on the similarity between the intrinsic text and its back-translation. The evaluation of detecting translated sentences with French shows that our method achieves 75.0% of both accuracy and F-score. It outperforms the existing methods whose the best accuracy is 62.8% and the F-score is 62.7%. The proposed method even detects more efficiently the back-translated text with 83.4% of accuracy, which is higher than 66.7% of the best previous accuracy. We also achieve similar results not only with F-score but also with similar experiments related to Japanese. Moreover, we prove that our detector can recognize both machine-translated and machine-back-translated texts without the language information which is used to generate these machine texts. It demonstrates the persistence of our method in various applications in both low- and rich-resource languages.Comment: INLG 2019, 9 page

Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments

We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing temporal order-preserving priors for obtaining the temporal similarity score between the videos. Moreover, we introduce a few-shot video classification framework that leverages the above appearance and temporal similarity scores across multiple steps, namely prototype-based training and testing as well as inductive and transductive prototype refinement. To the best of our knowledge, our work is the first to explore transductive few-shot video classification. Extensive experiments on both Kinetics and Something-Something V2 datasets show that both appearance and temporal alignments are crucial for datasets with temporal order sensitivity such as Something-Something V2. Our approach achieves similar or better results than previous methods on both datasets. Our code is available at https://github.com/VinAIResearch/fsvc-ata.Comment: Accepted to ECCV 202

AGent: A Novel Pipeline for Automatically Creating Unanswerable Questions

The development of large high-quality datasets and high-performing models have led to significant advancements in the domain of Extractive Question Answering (EQA). This progress has sparked considerable interest in exploring unanswerable questions within the EQA domain. Training EQA models with unanswerable questions helps them avoid extracting misleading or incorrect answers for queries that lack valid responses. However, manually annotating unanswerable questions is labor-intensive. To address this, we propose AGent, a novel pipeline that automatically creates new unanswerable questions by re-matching a question with a context that lacks the necessary information for a correct answer. In this paper, we demonstrate the usefulness of this AGent pipeline by creating two sets of unanswerable questions from answerable questions in SQuAD and HotpotQA. These created question sets exhibit low error rates. Additionally, models fine-tuned on these questions show comparable performance with those fine-tuned on the SQuAD 2.0 dataset on multiple EQA benchmarks.Comment: 16 pages, 10 tables, 3 figure

Determinants of poverty among ethnic minorities in the Northwest region, Vietnam

This paper investigates both community and household determinants of poverty among ethnic minorities in the Northwest region, Vietnam. Results of a fractional logit and a logit model show that some household factors such as fixed assets, education, land and off-farm employment have a reducing effect on both poverty intensity and incidence. Furthermore, some commune characteristics were found to be closely linked to poverty. Notably, the presence of means of transportation and post offices reduces both the poverty intensity and incidence. However, other commune and household factors affect only the poverty incidence or intensity, but not both. This suggests that previous studies that focused only on the determinants of poverty incidence using a logit/probit approach might not adequately evaluate or even ignored the impact of some factors on the poverty intensity. We draw both socio-economic household and commune level implications for poverty alleviation in the study area

Determinants of poverty among ethnic minorities in the Northwest region, Vietnam

This paper investigates both community and household determinants of poverty among ethnic minorities in the Northwest region, Vietnam. Results of a fractional logit and a logit model show that some household factors such as fixed assets, education, land and off-farm employment have a reducing effect on both poverty intensity and incidence. Furthermore, some commune characteristics were found to be closely linked to poverty. Notably, the presence of means of transportation and post offices reduces both the poverty intensity and incidence. However, other commune and household factors affect only the poverty incidence or intensity, but not both. This suggests that previous studies that focused only on the determinants of poverty incidence using a logit/probit approach might not adequately evaluate or even ignored the impact of some factors on the poverty intensity. We draw both socio-economic household and commune level implications for poverty alleviation in the study area

F2SD: A dataset for end-to-end group detection algorithms

The lack of large-scale datasets has been impeding the advance of deep learning approaches to the problem of F-formation detection. Moreover, most research works on this problem rely on input sensor signals of object location and orientation rather than image signals. To address this, we develop a new, large-scale dataset of simulated images for F-formation detection, called F-formation Simulation Dataset (F2SD). F2SD contains nearly 60,000 images simulated from GTA-5, with bounding boxes and orientation information on images, making it useful for a wide variety of modelling approaches. It is also closer to practical scenarios, where three-dimensional location and orientation information are costly to record. It is challenging to construct such a large-scale simulated dataset while keeping it realistic. Furthermore, the available research utilizes conventional methods to detect groups. They do not detect groups directly from the image. In this work, we propose (1) a large-scale simulation dataset F2SD and a pipeline for F-formation simulation, (2) a first-ever end-to-end baseline model for the task, and experiments on our simulation dataset.Comment: Accepted at ICMV 202