CMCL 2021 shared task on eye-tracking prediction

Workshop on Cognitive Modeling and Computational Linguistics (CMCL), June 10, 2021, Online Event202303 bcwwVersion of RecordSelf-fundedPublishe

Team DMG at CMCL 2022 Shared Task: Transformer Adapters for the Multi- and Cross-Lingual Prediction of Human Reading Behavior

In this paper, we present the details of our approaches that attained the second place in the shared task of the ACL 2022 Cognitive Modeling and Computational Linguistics Workshop. The shared task is focused on multi- and cross-lingual prediction of eye movement features in human reading behavior, which could provide valuable information regarding language processing. To this end, we train `adapters' inserted into the layers of frozen transformer-based pretrained language models. We find that multilingual models equipped with adapters perform well in predicting eye-tracking features. Our results suggest that utilizing language- and task-specific adapters is beneficial and translating test sets into similar languages that exist in the training set could help with zero-shot transferability in the prediction of human reading behavior

Less Descriptive yet Discriminative: Quantifying the Properties of Multimodal Referring Utterances via CLIP

In this work, we use a transformer-based pre-trained multimodal model, CLIP, to shed light on the mechanisms employed by human speakers when referring to visual entities. In particular, we use CLIP to quantify the degree of descriptiveness (how well an utterance describes an image in isolation) and discriminativeness (to what extent an utterance is effective in picking out a single image among similar images) of human referring utterances within multimodal dialogues. Overall, our results show that utterances become less descriptive over time while their discriminativeness remains unchanged. Through analysis, we propose that this trend could be due to participants relying on the previous mentions in the dialogue history, as well as being able to distill the most discriminative information from the visual context. In general, our study opens up the possibility of using this and similar models to quantify patterns in human data and shed light on the underlying cognitive mechanisms

Design for the distributed data locator service for multi-site data repositories

The Remote Experimentation Centre (REC) in Japan has been preparing to replicate the full dataset of ITER over 10 000 km distance. In such a multi-site data repository environment, the data location informing service will be essential to find and retrieve the data efficiently. Considering the long latency time and the self sustainability of remote sites, the data location database should be running at each repository site. Multi-master asynchronous replication between cooperating databases will be essential to realize the remote experimental collaborations in fusion research. This study has investigated the functional differences of some relational databases and found that Postgres BDR has the expected database replication capabilities. Bi-directional replication (BDR) tests by using the LHD database and SNET revealed that the throughputs are sufficient for remote collaborations in fusion experiments