Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBuilding a Video-and-Language Dataset with Human Actions for Multimodal Logical Inference
بناء مجموعة بيانات الفيديو واللغة مع إجراءات بشرية للاستدلال المنطقي متعدد الوسائط
368
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تقدم هذه الورقة مجموعة بيانات جديدة للفيديو واللغة مع إجراءات بشرية للاستدلال المنطقي متعدد الوسائط، والتي تركز على التعبيرات المتعمدة وجوقية تصف الإجراءات البشرية الديناميكية.تتكون DataSet من 200 فيديو، 5554 ملصقات عمل، و 1،942 ثلاثة توائم عمل من النموذج (الموضوع، المسند، كائن) يمكن ترجمته بسهولة إلى تمثيلات دلالية منطقية.من المتوقع أن تكون DataSet مفيدة لتقييم أنظمة الاستدلال متعددة الوسائط بين مقاطع الفيديو والجمل المعقدة الدلوية بما في ذلك النفي والكمية.
This paper introduces a new video-and-language dataset with human actions for multimodal logical inference, which focuses on intentional and aspectual expressions that describe dynamic human actions. The dataset consists of 200 videos, 5,554 action labels, and 1,942 action triplets of the form (subject, predicate, object) that can be easily translated into logical semantic representations. The dataset is expected to be useful for evaluating multimodal inference systems between videos and semantically complicated sentences including negation and quantification.
References used
https://aclanthology.org/
rate research
Read More
While many NLP pipelines assume raw, clean texts, many texts we encounter in the wild, including a vast majority of legal documents, are not so clean, with many of them being visually structured documents (VSDs) such as PDFs. Conventional preprocessi
ng tools for VSDs mainly focused on word segmentation and coarse layout analysis, whereas fine-grained logical structure analysis (such as identifying paragraph boundaries and their hierarchies) of VSDs is underexplored. To that end, we proposed to formulate the task as prediction of transition labels'' between text fragments that maps the fragments to a tree, and developed a feature-based machine learning system that fuses visual, textual and semantic cues. Our system is easily customizable to different types of VSDs and it significantly outperformed baselines in identifying different structures in VSDs. For example, our system obtained a paragraph boundary detection F1 score of 0.953 which is significantly better than a popular PDF-to-text tool with an F1 score of 0.739.
We investigate if a model can learn natural language with minimal linguistic input through interaction. Addressing this question, we design and implement an interactive language learning game that learns logical semantic representations compositional
ly. Our game allows us to explore the benefits of logical inference for natural language learning. Evaluation shows that the model can accurately narrow down potential logical representations for words over the course of the game, suggesting that our model is able to learn lexical mappings from scratch successfully.
Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks. However, its learning materials have been lacking a holistic organization by integrating resources from
various modalities, thereby lagging behind the research progress of this field. In this study, we release a full-scale multimodal dataset comprehensively gathering documents, summaries, images, captions, videos, audios, transcripts, and titles in English from CNN and Daily Mail. To our best knowledge, this is the first collection that spans all modalities and nearly comprises all types of materials available in this community. In addition, we devise a baseline model based on the novel dataset, which employs a newly proposed Jump-Attention mechanism based on transcripts. The experimental results validate the important assistance role of the external information for multimodal summarization.
We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020). We demonstrate this system's capacity to handle a variety of challenging semantic phenomena using the FraCaS d
ataset (Cooper et al., 1996). These results give empirical evidence for prior claims that ULF is an appropriate representation to mediate natural logic-like inferences.
Reviewing contracts is a time-consuming procedure that incurs large expenses to companies and social inequality to those who cannot afford it. In this work, we propose document-level natural language inference (NLI) for contracts'', a novel, real-wor
ld application of NLI that addresses such problems. In this task, a system is given a set of hypotheses (such as Some obligations of Agreement may survive termination.'') and a contract, and it is asked to classify whether each hypothesis is entailed by'', contradicting to'' or not mentioned by'' (neutral to) the contract as well as identifying evidence'' for the decision as spans in the contract. We annotated and release the largest corpus to date consisting of 607 annotated contracts. We then show that existing models fail badly on our task and introduce a strong baseline, which (a) models evidence identification as multi-label classification over spans instead of trying to predict start and end tokens, and (b) employs more sophisticated context segmentation for dealing with long documents. We also show that linguistic characteristics of contracts, such as negations by exceptions, are contributing to the difficulty of this task and that there is much room for improvement.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
