Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGiBERT: Enhancing BERT with Linguistic Information using a Lightweight Gated Injection Method
جيبيرت: تعزيز بيرت مع المعلومات اللغوية باستخدام طريقة حقن بوازم خفيفة الوزن
375
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
كانت النماذج اللغوية الكبيرة المدربة مسبقا مثل بيرت القوة الدافعة وراء التحسينات الأخيرة في العديد من مهام NLP.ومع ذلك، يتم تدريب بيرت فقط على التنبؤ بالكلمات المفقودة - إما من خلال اخفاء أو تنبؤ الجملة التالي - وليس لديه معرفة بالمعلومات المعجمية أو النحوية أو الدلالية التي تتجاوز ما يلتقطه من خلال التدريب المسبق غير المدعوم.نقترح طريقة جديدة لحقن المعلومات اللغوية بشكل صريح في شكل embeddings في أي طبقة من بيرت المدرب مسبقا.عند ضمانات المضادات المضادة ومقرها التبعية، تشير تحسينات الأداء على مجموعات بيانات التشابه الدلالية المتعددة إلى أن هذه المعلومات مفيدة وفقدها حاليا من النموذج الأصلي.يوضح تحليلنا النوعي أن حقن التضمين المضاد للأدمان مفيد بشكل خاص، مع تحسينات ملحوظة على الأمثلة التي تتطلب دقة مرادف.
Large pre-trained language models such as BERT have been the driving force behind recent improvements across many NLP tasks. However, BERT is only trained to predict missing words -- either through masking or next sentence prediction -- and has no knowledge of lexical, syntactic or semantic information beyond what it picks up through unsupervised pre-training. We propose a novel method to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT. When injecting counter-fitted and dependency-based embeddings, the performance improvements on multiple semantic similarity datasets indicate that such information is beneficial and currently missing from the original model. Our qualitative analysis shows that counter-fitted embedding injection is particularly beneficial, with notable improvements on examples that require synonym resolution.
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.
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.
The current natural language processing is strongly focused on raising accuracy. The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters. However, simple syntactic tasks such as part-of-speech (POS)
tagging, dependency parsing or named entity recognition (NER) do not require the largest models to achieve acceptable results. In line with this assumption we try to minimize the size of the model that jointly performs all three tasks. We introduce ComboNER: a lightweight tool, orders of magnitude smaller than state-of-the-art transformers. It is based on pre-trained subword embeddings and recurrent neural network architecture. ComboNER operates on Polish language data. The model has outputs for POS tagging, dependency parsing and NER. Our paper contains some insights from fine-tuning of the model and reports its overall results.
People convey their intention and attitude through linguistic styles of the text that they write. In this study, we investigate lexicon usages across styles throughout two lenses: human perception and machine word importance, since words differ in th
e strength of the stylistic cues that they provide. To collect labels of human perception, we curate a new dataset, Hummingbird, on top of benchmarking style datasets. We have crowd workers highlight the representative words in the text that makes them think the text has the following styles: politeness, sentiment, offensiveness, and five emotion types. We then compare these human word labels with word importance derived from a popular fine-tuned style classifier like BERT. Our results show that the BERT often finds content words not relevant to the target style as important words used in style prediction, but humans do not perceive the same way even though for some styles (e.g., positive sentiment and joy) human- and machine-identified words share significant overlap for some styles.
Linguistic typology is an area of linguistics concerned with analysis of and comparison between natural languages of the world based on their certain linguistic features. For that purpose, historically, the area has relied on manual extraction of lin
guistic feature values from textural descriptions of languages. This makes it a laborious and time expensive task and is also bound by human brain capacity. In this study, we present a deep learning system for the task of automatic extraction of linguistic features from textual descriptions of natural languages. First, textual descriptions are manually annotated with special structures called semantic frames. Those annotations are learned by a recurrent neural network, which is then used to annotate un-annotated text. Finally, the annotations are converted to linguistic feature values using a separate rule based module. Word embeddings, learned from general purpose text, are used as a major source of knowledge by the recurrent neural network. We compare the proposed deep learning system to a previously reported machine learning based system for the same task, and the deep learning system wins in terms of F1 scores with a fair margin. Such a system is expected to be a useful contribution for the automatic curation of typological databases, which otherwise are manually developed.
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
