Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLanguage in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
اللغة في المربع (البحث): تعلم لغة التأريض في تفاعل العالم الحقيقي
471
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نحن نحقق في تعلم اللغة الأساسية من خلال بيانات عالمية حقيقية، من خلال نمذجة ديناميات متعلم المعلم من خلال التفاعلات الطبيعية التي تحدث بين المستخدمين ومحركات البحث؛على وجه الخصوص، نستكشف ظهور التعميم الدلالي من تمثيلات كثيفة غير مخالفة خارج البيئات الاصطناعية.يتم تعلم مجال التأريض وظيفة دلالة ودالة تكوين من بيانات المستخدم فقط.نظهر كيف تظهر الدلالات الناتجة عن عبارات الاسم خصائصا تتراكم بينما تكون مائيا تماما دون أي وضع علامات واضحة.نحن نقسم لدينا دلالاتنا المتطرفة على التركيبية ومهام الاستدلال صفرية، ونرى أنها توفر نتائج أفضل وتعميمات أفضل من نماذج SOTA غير المدرجة، مثل Word2VEC و BERT.
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments. A grounding domain, a denotation function and a composition function are learned from user data only. We show how the resulting semantics for noun phrases exhibits compositional properties while being fully learnable without any explicit labelling. We benchmark our grounded semantics on compositionality and zero-shot inference tasks, and we show that it provides better results and better generalizations than SOTA non-grounded models, such as word2vec and BERT.
References used
https://aclanthology.org/
rate research
Read More
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising app
roach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.
Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web sea
rch with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.
We analyse how a transformer-based language model learns the rules of chess from text data of recorded games. We show how it is possible to investigate how the model capacity and the available number of training data influence the learning success of
a language model with the help of chess-specific metrics. With these metrics, we show that more games used for training in the studied range offers significantly better results for the same training time. However, model size does not show such a clear influence. It is also interesting to observe that the usual evaluation metrics for language models, predictive accuracy and perplexity, give no indication of this here. Further examination of trained models reveals how they store information about board state in the activations of neuron groups, and how the overall sequence of previous moves influences the newly-generated moves.
This is a research proposal for doctoral research into sarcasm detection, and the real-time compilation of an English language corpus of sarcastic utterances. It details the previous research into similar topics, the potential research directions and the research aims.
Question answering (QA) systems are now available through numerous commercial applications for a wide variety of domains, serving millions of users that interact with them via speech interfaces. However, current benchmarks in QA research do not accou
nt for the errors that speech recognition models might introduce, nor do they consider the language variations (dialects) of the users. To address this gap, we augment an existing QA dataset to construct a multi-dialect, spoken QA benchmark on five languages (Arabic, Bengali, English, Kiswahili, Korean) with more than 68k audio prompts in 24 dialects from 255 speakers. We provide baseline results showcasing the real-world performance of QA systems and analyze the effect of language variety and other sensitive speaker attributes on downstream performance. Last, we study the fairness of the ASR and QA models with respect to the underlying user populations.
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
