Visually Grounded Continual Learning of Compositional Phrases
Xisen Jin | Junyi Du | Arka Sadhu | Ram Nevatia | Xiang Ren
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes. In the task, models are trained on a paired image-caption stream which has shifting object distribution; while being constantly evaluated by a visually-grounded masked language prediction task on held-out test sets. VisCOLL compounds the challenges of continual learning (i.e., learning from continuously shifting data distribution) and compositional generalization (i.e., generalizing to novel compositions). To facilitate research on VisCOLL, we construct two datasets, COCO-shift and Flickr-shift, and benchmark them using different continual learning methods. Results reveal that SoTA continual learning approaches provide little to no improvements on VisCOLL, since storing examples of all possible compositions is infeasible. We conduct further ablations and analysis to guide future work.
A Benchmark for Structured Procedural Knowledge Extraction from Cooking Videos
Frank F. Xu | Lei Ji | Botian Shi | Junyi Du | Graham Neubig | Yonatan Bisk | Nan Duan
Proceedings of the First International Workshop on Natural Language Processing Beyond Text
Watching instructional videos are often used to learn about procedures. Video captioning is one way of automatically collecting such knowledge. However, it provides only an indirect, overall evaluation of multimodal models with no finer-grained quantitative measure of what they have learned. We propose instead, a benchmark of structured procedural knowledge extracted from cooking videos. This work is complementary to existing tasks, but requires models to produce interpretable structured knowledge in the form of verb-argument tuples. Our manually annotated open-vocabulary resource includes 356 instructional cooking videos and 15,523 video clip/sentence-level annotations. Our analysis shows that the proposed task is challenging and standard modeling approaches like unsupervised segmentation, semantic role labeling, and visual action detection perform poorly when forced to predict every action of a procedure in a structured form.
Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis
Junyi Du | He Jiang | Jiaming Shen | Xiang Ren
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce human efforts and scale the process, automated CTA transcript parsing is desirable. However, this task has unique challenges as (1) it requires the understanding of long-range context information in conversational text; and (2) the amount of labeled data is limited and indirect—i.e., context-aware, noisy, and low-resource. In this paper, we propose a weakly-supervised information extraction framework for automated CTA transcript parsing. We partition the parsing process into a sequence labeling task and a text span-pair relation extraction task, with distant supervision from human-curated protocol files. To model long-range context information for extracting sentence relations, neighbor sentences are involved as a part of input. Different types of models for capturing context dependency are then applied. We manually annotate real-world CTA transcripts to facilitate the evaluation of the parsing tasks.
- Xiang Ren 2
- Xisen Jin 1
- Arka Sadhu 1
- Ram Nevatia 1
- Frank F. Xu 1
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