Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Analysis of State-of-the-Art Models for Situated Interactive MultiModal Conversations (SIMMC)
تحليل النماذج الحديثة للمحادثات متعددة الوسائط التفاعلية (SIMMC)
575
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
هناك اهتمام متزايد بالمساعدين الظاهريين مع قدرات متعددة الوسائط، على سبيل المثال، استنتاج سياق محادثة من خلال فهم المشهد. تعالج مجموعة بيانات محادثات متعددة الوسائط المتعددة (SIMMC) التي تم إصدارها مؤخرا هذا الاتجاه من خلال تمكين البحث عن إنشاء مساعدين افتراضي، قادرين على مراعاة المشهد الذي يراه المستخدم عند التحدث مع المستخدم ويتفاعل أيضا مع العناصر الموجودة في المشهد. DataSet SIMMC هو رواية في أنه يحتوي على حوار مساعد للمستخدم المشروح بالكامل، ومساعدات موجهة نحو المهام حيث يراقب المستخدم ومشاركة مساعد نفس العناصر المرئية والأخير إجراء إجراءات لتحديث المشهد. أدفع تحدي SIMMC، الذي عقد كجزء من تحدي تكنولوجيا نظام الحوار ثمين (DSTC9)، تطوير النماذج المختلفة التي تضع معا مجموعة جديدة من الفن في مجموعة بيانات SIMMC. في هذا العمل، قارننا وتحليل هذه النماذج لتحديد ما عملت؟ "، والفجوات المتبقية؛ Whatnext؟ '. يوضح تحليلنا أنه على الرغم من أن نماذج اللغة المحددة مسبقا تتكيف مع هذه المجموعة تظهر وعد كبير، فهناك مؤشرات على أن السياق المتعدد غير المستخدمة بالكامل، وهناك حاجة إلى تكامل قاعدة معرفة أفضل وقابل للتطوير. نأمل أن يوفر هذا التحليل الأول من نوع نماذج SIMMC رؤى وفرصا مفيدا لمزيد من البحث في وكلاء المحادثة متعددة الوسائط
There is a growing interest in virtual assistants with multimodal capabilities, e.g., inferring the context of a conversation through scene understanding. The recently released situated and interactive multimodal conversations (SIMMC) dataset addresses this trend by enabling research to create virtual assistants, which are capable of taking into account the scene that user sees when conversing with the user and also interacting with items in the scene. The SIMMC dataset is novel in that it contains fully annotated user-assistant, task-orientated dialogs where the user and an assistant co-observe the same visual elements and the latter can take actions to update the scene. The SIMMC challenge, held as part of theNinth Dialog System Technology Challenge(DSTC9), propelled the development of various models which together set a new state-of-the-art on the SIMMC dataset. In this work, we compare and analyze these models to identifywhat worked?', and the remaining gaps;whatnext?'. Our analysis shows that even though pretrained language models adapted to this set-ting show great promise, there are indications that multimodal context isn't fully utilised, and there is a need for better and scalable knowledge base integration. We hope this first-of-its-kind analysis for SIMMC models provides useful insights and opportunities for further research in multimodal conversational agents
References used
https://aclanthology.org/
rate research
Read More
Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emotion in multimodal data. The Lat
e Fusion model merges unimodal features to create a multimodal feature sequence, the Round Robin model iteratively combines bimodal features using cross-modal attention, and the Hybrid Fusion model combines trimodal and unimodal features together to form a final feature sequence for predicting sentiment. Our experiments show that our small models are effective and outperform the publicly released versions of much larger, state-of-the-art multimodal sentiment analysis systems.
Despite the increasingly good quality of Machine Translation (MT) systems, MT outputs require corrections. Automatic Post-Editing (APE) models have been introduced to perform these corrections without human intervention. However, no system has been a
ble to fully automate the Post-Editing (PE) process. Moreover, while numerous translation tools, such as Translation Memories (TMs), largely benefit from translators' input, Human-Computer Interaction (HCI) remains limited when it comes to PE. This research-in-progress paper discusses APE models and suggests that they could be improved in more interactive scenarios, as previously done in MT with the creation of Interactive MT (IMT) systems. Based on the hypothesis that PE would benefit from HCI, two methodologies are proposed. Both suggest that traditional batch learning settings are not optimal for PE. Instead, online techniques are recommended to train and update PE models on the fly, via either real or simulated interactions with the translator.
Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering. However, all these explorations pr
oduce a unimodal textual output as the answer. In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal. Through human experiments, we empirically show that such multimodal outputs provide better cognitive understanding of the answers. We also propose a novel multimodal question-answering framework, MExBERT, that incorporates a joint textual and visual attention towards producing such a multimodal output. Our method relies on a novel multimodal dataset curated for this problem from publicly available unimodal datasets. We show the superior performance of MExBERT against strong baselines on both the automatic as well as human metrics.
This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextual multilingual multimodal embeddings. Under a zero-s
hot setting, we empirically demonstrate that performance degrades significantly when we query the multilingual text-video model with non-English sentences. To address this problem, we introduce a multilingual multimodal pre-training strategy, and collect a new multilingual instructional video dataset (Multi-HowTo100M) for pre-training. Experiments on VTT show that our method significantly improves video search in non-English languages without additional annotations. Furthermore, when multilingual annotations are available, our method outperforms recent baselines by a large margin in multilingual text-to-video search on VTT and VATEX; as well as in multilingual text-to-image search on Multi30K. Our model and Multi-HowTo100M is available at http://github.com/berniebear/Multi-HT100M.
Abstract Large-scale pretraining and task-specific fine- tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of methods have been proposed for pretraining vision and language
BERTs to tackle challenges at the intersection of these two key areas of AI. These models can be categorized into either single-stream or dual-stream encoders. We study the differences between these two categories, and show how they can be unified under a single theoretical framework. We then conduct controlled experiments to discern the empirical differences between five vision and language BERTs. Our experiments show that training data and hyperparameters are responsible for most of the differences between the reported results, but they also reveal that the embedding layer plays a crucial role in these massive models.
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
