Dual Attention Networks for Visual Reference Resolution in Visual Dialog

Visual dialog (VisDial) is a task which requires an AI agent to answer a series of questions grounded in an image. Unlike in visual question answering (VQA), the series of questions should be able to capture a temporal context from a dialog history and exploit visually-grounded information. A problem called visual reference resolution involves these challenges, requiring the agent to resolve ambiguous references in a given question and find the references in a given image. In this paper, we propose Dual Attention Networks (DAN) for visual reference resolution. DAN consists of two kinds of attention networks, REFER and FIND. Specifically, REFER module learns latent relationships between a given question and a dialog history by employing a self-attention mechanism. FIND module takes image features and reference-aware representations (i.e., the output of REFER module) as input, and performs visual grounding via bottom-up attention mechanism. We qualitatively and quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing that DAN outperforms the previous state-of-the-art model by a significant margin.Comment: EMNLP 201

Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models

The present study aims to investigate similarities between how humans and connectionist models experience difficulty in arithmetic problems. Problem difficulty was operationalized by the number of carries involved in solving a given problem. Problem difficulty was measured in humans by response time, and in models by computational steps. The present study found that both humans and connectionist models experience difficulty similarly when solving binary addition and subtraction. Specifically, both agents found difficulty to be strictly increasing with respect to the number of carries. Another notable similarity is that problem difficulty increases more steeply in subtraction than in addition, for both humans and connectionist models. Further investigation on two model hyperparameters --- confidence threshold and hidden dimension --- shows higher confidence thresholds cause the model to take more computational steps to arrive at the correct answer. Likewise, larger hidden dimensions cause the model to take more computational steps to correctly answer arithmetic problems; however, this effect by hidden dimensions is negligible.Comment: 7 pages; 15 figures; 5 tables; Published in the proceedings of the 17th International Conference on Cognitive Modelling (ICCM 2019

Learning to Write with Coherence From Negative Examples

Coherence is one of the critical factors that determine the quality of writing. We propose writing relevance (WR) training method for neural encoder-decoder natural language generation (NLG) models which improves coherence of the continuation by leveraging negative examples. WR loss regresses the vector representation of the context and generated sentence toward positive continuation by contrasting it with the negatives. We compare our approach with Unlikelihood (UL) training in a text continuation task on commonsense natural language inference (NLI) corpora to show which method better models the coherence by avoiding unlikely continuations. The preference of our approach in human evaluation shows the efficacy of our method in improving coherence.Comment: 4+1 pages, 4 figures, 2 tables. ICASSP 2022 rejecte

Effect of active learning and online discussions on the academic performances of dental students

COVID-19 caused significant confusion around the world, and dental education was no exception. Therefore, in line with the demands of the times, this study sought to determine the applicability of online active learning to dental education. This study was conducted in the second semester of 2020 at a school of dentistry in a selective university in Korea. A total of 114 dental students were recruited. Participants were assigned to four different groups (lecture and discussion [LD], lecture and discussion with instructors worksheet [LW], self-study and discussion [SSD], and self-study and discussion with instructors worksheet [SW]) using the random breakout room function in the Zoom video conference application. Their final test scores were then analyzed using analysis of variance and the online active learning results were compared with the offline learning results. The scores were highest for the transfer type items in the SSD group, followed by the SW group and the two lecture groups, which had no significant differences. These scores and pattern differences between the groups were similar for all items. The results suggested that studying by oneself rather than simply listening to lectures enhanced the effects of the discussions and led to higher learning outcomes. In addition, the effect of the instructor's intervention in the middle of the discussion varied depending on the pre-learning activities of discussion.As with previous offline experiments, self-study followed by group discussion had higher learning outcomes for both the verbatim and transfer type items. In agreement with the Interactive, Constructive, Active, and Passive (ICAP)framework and other active learning theories, the findings clearly indicated that online active learning was applicable to dental students, and when self-study precedes discussion, the learning is richer and the learning outcomes are better

The impact of writing on academic performance for medical students

Abstract Background Writing is a useful learning activity that promotes higher-order thinking, but there are limited studies that prove its effectiveness. In previous research, researchers tested the effect of summary writing on students comprehension and found no significant difference from that of re-studying texts. The purpose of this study, therefore, is to expand previous findings and investigate the effect of two types of writing tasks on medical students academic performance, specifically in the transfer of knowledge. Methods An experiment was conducted with 139 medical students from Seoul National University College of Medicine. They were randomly assigned to three study conditions: self-study (SS), expository writing (EW), and argumentative writing (AW) group. Each group studied the given material by the method they were assigned, and they were tested on their comprehension and transfer of knowledge using rote-memory type items and transfer type items respectively. Results The results showed that the two writing groups displayed better performance than the SS group in transfer type items, while there was no difference in scores between the EW and AW group. However, the three groups showed no significant difference in their scores for rote-memory type items. Also, there was a positive correlation between the writing scores and transfer type item scores in the AW group. Conclusions This study provides empirical evidence for writing to be adopted in medical education for greater educational benefits. Our findings indicate that writing can enhance learning and higher-order thinking, which are critical for medical students

Active learning through discussion: ICAP framework for education in health professions

Background The ICAP framework based on cognitive science posits four modes of cognitive engagement: Interactive, Constructive, Active, and Passive. Focusing on the wide applicability of discussion as interactive engagement in medical education, we investigated the effect of discussion when it was preceded by self-study and further investigated the effect of generating questions before discussions. Methods This study was conducted in the second semester of 2018 and was participated in by 129 students majoring in health professions, including medicine, dentistry, veterinary medicine, and nursing. The students were assigned to four different trial groups and were asked to fill out a Subjective Mental Effort Questionnaire after completing each session. Their performance in posttest scores was analyzed using Bonferroni test, and mental effort was analyzed using mediation analysis. Results These results indicated that the self-study and question group had the highest performance and that the lecture and summary group had the lowest performance when comparing the total score. Using the analysis of mental effort, it was confirmed that the relationship between different study conditions and post-test performance was mediated by mental effort during test. Conclusions Our findings support the ICAP framework and provide practical implications for medical education, representing the fact that students learn more when they are involved in active learning activities, such as self-study and question generation, prior to discussions.This work was supported by Research Resettlement Fund, funded by Seoul National University to allow the new faculty to design and develop new teaching and learning paradigms in higher education

Problem difficulty in arithmetic cognition: Humans and connectionist models

N

Emotion Evaluator: Expanding the Affective Lexicon with Neural Network Model

Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened