Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLightweight Models for Multimodal Sequential Data
نماذج خفيفة الوزن للبيانات المتسلسلة متعددة الوسائط
321
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تشمل اللغة البشرية أكثر من مجرد نص؛كما أنه ينقل العواطف من خلال النغمة والإيماءات.نقدم دراسة حالة لثلاث هندسة بسيطة وفعالة قائمة على المحولات لتنبؤ المعنويات والعاطفة في البيانات متعددة الوسائط.يقوم نموذج الانصهار المتأخر بدمج ميزات Unimodal لإنشاء تسلسل ميزة متعددة الوسائط، يجمع نموذج Robin Robin بشكل متكرر بين ميزات BIMODAL باستخدام اهتماما عبر الوسائط، ويجمع نموذج الانصهار الهجين بين ميزات Trimodal و Unimodal معا لتشكيل تسلسل مائع نهائي للتنبؤ بالمشاعر.تبين تجاربنا أن نماذجنا الصغيرة فعالة ومتفوقة على الإصدارات التي تم إصدارها علنا من أنظمة تحليل المعنويات متعددة الوسائط الأكبر والحديثة.
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 Late 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.
References used
https://aclanthology.org/
rate research
Read More
Best-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances. Studies have shown the efficacy of this methodology applied to NLP tasks in terms of a higher
quality of the datasets produced by following it. In this system demonstration paper, we present Litescale, a free software library to create and manage BWS annotation tasks. Litescale computes the tuples to annotate, manages the users and the annotation process, and creates the final gold standard. The functionalities of Litescale can be accessed programmatically through a Python module, or via two alternative user interfaces, a textual console-based one and a graphical Web-based one. We further developed and deployed a fully online version of Litescale complete with multi-user support.
Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hype
rbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.
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.
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.
An understanding of humor is an essential component of human-facing NLP systems. In this paper, we investigate several methods for detecting humor in short statements as part of Semeval-2021 Shared Task 7. For Task 1a, we apply an ensemble of fine-tu
ned pre-trained language models; for Tasks 1b, 1c, and 2a, we investigate various tree-based and linear machine learning models. Our final system achieves an F1-score of 0.9571 (ranked 24 / 58) on Task 1a, an RMSE of 0.5580 (ranked 18 / 50) on Task 1b, an F1-score of 0.5024 (ranked 26 / 36) on Task 1c, and an RMSE of 0.7229 (ranked 45 / 48) on Task 2a.
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
