Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userProbing Pre-trained Language Models for Semantic Attributes and their Values
التحقيق نماذج اللغة المدربة مسبقا للسمات الدلالية وقيمها
215
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
نماذج اللغة المحددة مسبقا (PTLMS) تسفر عن الأداء الحديث في العديد من مهام معالجة اللغة الطبيعية، بما في ذلك بناء الجملة والدلالات والعموم.في هذه الورقة، نركز على التعرف على أي مدى تلتقط PTLMS السمات الدلالية وقيمها، على سبيل المثال، الارتباط بين القيمة الغنية والعالية الصافية.نستخدم ptlms للتنبؤ الرموز الملثمين باستخدام أنماط وقوائم العناصر من Wikidata من أجل التحقق من مدى احتمال ترميز PTLMS السمات الدلالية جنبا إلى جنب مع قيمها.مثل هذه الاستنتاجات القائمة على دلالات بديهية للبشر كجزء من فهم لغتنا.نظرا لأن PTLMS يتم تدريبها على كمية كبيرة من بيانات ويكيبيديا، فسوف نفترض أنها يمكن أن تولد تنبؤات مماثلة، ومع ذلك تكشف نتائجنا أن PTLMS لا تزال أسوأ بكثير من البشر في هذه المهمة.نوضح الأدلة والتحليل في شرح كيفية استغلال منهجيةنا لدمج سياق ودواني أفضل في PTLMS باستخدام قواعد المعرفة.
Pretrained language models (PTLMs) yield state-of-the-art performance on many natural language processing tasks, including syntax, semantics and commonsense. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g., the correlation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on semantics are intuitive for humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data we would assume that they can generate similar predictions, yet our findings reveal that PTLMs are still much worse than humans on this task. We show evidence and analysis explaining how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.
References used
https://aclanthology.org/
rate research
Read More
Modern transformer-based language models are revolutionizing NLP. However, existing studies into language modelling with BERT have been mostly limited to English-language material and do not pay enough attention to the implicit knowledge of language,
such as semantic roles, presupposition and negations, that can be acquired by the model during training. Thus, the aim of this study is to examine behavior of the model BERT in the task of masked language modelling and to provide linguistic interpretation to the unexpected effects and errors produced by the model. For this purpose, we used a new Russian-language dataset based on educational texts for learners of Russian and annotated with the help of the National Corpus of the Russian language. In terms of quality metrics (the proportion of words, semantically related to the target word), the multilingual BERT is recognized as the best model. Generally, each model has distinct strengths in relation to a certain linguistic phenomenon. These observations have meaningful implications for research into applied linguistics and pedagogy, contribute to dialogue system development, automatic exercise making, text generation and potentially could improve the quality of existing linguistic technologies
Pre-trained language models (PrLM) have to carefully manage input units when training on a very large text with a vocabulary consisting of millions of words. Previous works have shown that incorporating span-level information over consecutive words i
n pre-training could further improve the performance of PrLMs. However, given that span-level clues are introduced and fixed in pre-training, previous methods are time-consuming and lack of flexibility. To alleviate the inconvenience, this paper presents a novel span fine-tuning method for PrLMs, which facilitates the span setting to be adaptively determined by specific downstream tasks during the fine-tuning phase. In detail, any sentences processed by the PrLM will be segmented into multiple spans according to a pre-sampled dictionary. Then the segmentation information will be sent through a hierarchical CNN module together with the representation outputs of the PrLM and ultimately generate a span-enhanced representation. Experiments on GLUE benchmark show that the proposed span fine-tuning method significantly enhances the PrLM, and at the same time, offer more flexibility in an efficient way.
Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level
relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse --- but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models.
In this study, we propose a self-supervised learning method that distils representations of word meaning in context from a pre-trained masked language model. Word representations are the basis for context-aware lexical semantics and unsupervised sema
ntic textual similarity (STS) estimation. A previous study transforms contextualised representations employing static word embeddings to weaken excessive effects of contextual information. In contrast, the proposed method derives representations of word meaning in context while preserving useful context information intact. Specifically, our method learns to combine outputs of different hidden layers using self-attention through self-supervised learning with an automatically generated training corpus. To evaluate the performance of the proposed approach, we performed comparative experiments using a range of benchmark tasks. The results confirm that our representations exhibited a competitive performance compared to that of the state-of-the-art method transforming contextualised representations for the context-aware lexical semantic tasks and outperformed it for STS estimation.
Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive use
ful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.
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
