Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userConditional probing: measuring usable information beyond a baseline
التحقيق الشرطي: قياس المعلومات القابلة للاستخدام وراء خط الأساس
93
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
التحقيق تجارب التحقيق في مدى جعل التمثيل العصبي الممتلكات - مثل جزء من الكلام --- يمكن التنبؤ بها. يشير المرء إلى أن التمثيل يرمز عقار إذا كان التحقيق في هذا التمثيل ينتج دقة أعلى من التحقيق في تمثيل خط أساس مثل تضمين الكلمات غير السياقية. بدلا من استخدام خطوط الأساس كنقطة مقارنة، نحن مهتمون بقياس المعلومات الواردة في التمثيل ولكن ليس في الأساس. على سبيل المثال، يمكن للطرق الحالية اكتشافها عندما يكون التمثيل مفيدا أكثر من هوية الكلمة (خط الأساس) للتنبؤ بجزء خطاب؛ ومع ذلك، لا يمكنهم اكتشاف متى تمثل التمثيل من مجرد جوانب جزء من الكلام، ولا يفسره كلمة الهوية. في هذا العمل، نقوم بتوسيع نظرية المعلومات القابلة للاستخدام المسماة معلومات V واقتراح التحقيق الشريطي، والذي ظروف صراحة بشأن المعلومات في الأساس. في دراسة الحالة، نجد أنه بعد تكييفها على أشرطة الكلمات غير السياقية، يمكن الوصول إلى العقارات مثل جزء من الكلام في طبقات أعمق من الشبكة مما كان يعتقد سابقا.
Probing experiments investigate the extent to which neural representations make properties---like part-of-speech---predictable. One suggests that a representation encodes a property if probing that representation produces higher accuracy than probing a baseline representation like non-contextual word embeddings. Instead of using baselines as a point of comparison, we're interested in measuring information that is contained in the representation but not in the baseline. For example, current methods can detect when a representation is more useful than the word identity (a baseline) for predicting part-of-speech; however, they cannot detect when the representation is predictive of just the aspects of part-of-speech not explainable by the word identity. In this work, we extend a theory of usable information called V-information and propose conditional probing, which explicitly conditions on the information in the baseline. In a case study, we find that after conditioning on non-contextual word embeddings, properties like part-of-speech are accessible at deeper layers of a network than previously thought.
References used
https://aclanthology.org/
rate research
Read More
This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice). The pipeline is composed of a retriever, passage reranker, extractive reader, generative reader and a mechanism that aggregates the final prediction from al
l system's components. We demonstrate its strength across three open-domain QA datasets: NaturalQuestions, TriviaQA and EfficientQA, surpassing state-of-the-art on the first two. Our analysis demonstrates that: (i) combining extractive and generative reader yields absolute improvements up to 5 exact match and it is at least twice as effective as the posterior averaging ensemble of the same models with different parameters, (ii) the extractive reader with fewer parameters can match the performance of the generative reader on extractive QA datasets.
This Paper aims to Studying the factors of Quality measurement in information
systems applied in telecommunications companies. The study is divided into four
main factors; the first factor is time which consists of four sub factors (Timelines,
upd
ating, Frequency, and Availability). Second is Form which includes Five sub
factors (Clarity, Details, Presentation, Order, Media).Third is Content which
includes nine sub factors (Accuracy, Relevance, Completeness, Concinseness, Scope,
Security, Reliability, Economy, Competency).Technical, the final factors, it consists
of two sub factors (Software Quality, Hardware Quality). We have tested this model
in Syriatel Company to prove its validity, and applicability in many other types of
organizations. Time and content factors was the final result for factors of Quality
measurement in information systems applied in the syriatel company.
This paper describes the submission by the team from the Department of Computational Linguistics, Zurich University, to the Multilingual Grapheme-to-Phoneme Conversion (G2P) Task 1 of the SIGMORPHON 2021 challenge in the low and medium settings. The
submission is a variation of our 2020 G2P system, which serves as the baseline for this year's challenge. The system is a neural transducer that operates over explicit edit actions and is trained with imitation learning. For this challenge, we experimented with the following changes: a) emitting phoneme segments instead of single character phonemes, b) input character dropout, c) a mogrifier LSTM decoder (Melis et al., 2019), d) enriching the decoder input with the currently attended input character, e) parallel BiLSTM encoders, and f) an adaptive batch size scheduler. In the low setting, our best ensemble improved over the baseline, however, in the medium setting, the baseline was stronger on average, although for certain languages improvements could be observed.
In this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. A model for this task should be able to extract high-level patterns such
as presence of a peak or a dip. While typical contemporary neural models with attention mechanisms can generate fluent output descriptions for this task, they often generate factually incorrect descriptions. We propose a computational model with a truth-conditional architecture which first runs small learned programs on the input time series, then identifies the programs/patterns which hold true for the given input, and finally conditions on *only* the chosen valid program (rather than the input time series) to generate the output text description. A program in our model is constructed from modules, which are small neural networks that are designed to capture numerical patterns and temporal information. The modules are shared across multiple programs, enabling compositionality as well as efficient learning of module parameters. The modules, as well as the composition of the modules, are unobserved in data, and we learn them in an end-to-end fashion with the only training signal coming from the accompanying natural language text descriptions. We find that the proposed model is able to generate high-precision captions even though we consider a small and simple space of module types.
We propose using a multilabel probing task to assess the morphosyntactic representations of multilingual word embeddings. This tweak on canonical probing makes it easy to explore morphosyntactic representations, both holistically and at the level of
individual features (e.g., gender, number, case), and leads more naturally to the study of how language models handle co-occurring features (e.g., agreement phenomena). We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we verify that multilingual BERT renders many morphosyntactic features simultaneously extractable. We further evaluate the probes on six held-out languages: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This zero-shot style of probing has the added benefit of revealing which cross-linguistic properties a language model recognizes as being shared by multiple languages.
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
