Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
تشفير ضبط الدقيقة لتحسين النمذجة النمذجة النمذجة النمذجة
280
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
يمكن أن تصدر نماذج الموضوع العصبي أو استبدال مدخلات كيس الكلمات مع التمثيلات المستفادة من نماذج التنبؤ بكلمة التنبؤ المدربة مسبقا مسبقا. تتمثل إحدى فائدة واحدة عند استخدام التمثيلات من النماذج متعددة اللغات هي أنها تسهل نمذجة موضوع الصلاع اللاحق للصفر. ومع ذلك، في حين أنه لوحظ على نطاق واسع أن المدينات المدربة مسبقا يجب أن يتم ضبطها بشكل جيد لمهمة معينة، فليس من الواضح على الفور ما يجب أن يبدو الإشراف بهذه المهمة غير المزدوجة مثل نمذجة الموضوع. وبالتالي، نقترح عدة طرق لترميز التركيز الدقيق لتحسين كل من النمذجة النمذجة العصبية أحادية الألوان والصفرية. نحن نفكر في ضبط المهام الإضافية، بناء مهمة تصنيف موضوع جديد، دمج هدف تصنيف الموضوع بشكل مباشر في التدريب النموذجي للموضوع، واستمر التدريب قبل التدريب. نجد أن تمثيل تشفير الترميز بشكل جيد على تصنيف الموضوع وإدماج مهمة تصنيف الموضوع مباشرة في نمذجة موضوع يحسن جودة الموضوع، وأن تمثيل التشفير الدقيق في أي مهمة في أي مهمة هي أهم عامل لتسهيل النقل عبر اللغات.
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.
References used
https://aclanthology.org/
rate research
Read More
Topic models are useful tools for analyzing and interpreting the main underlying themes of large corpora of text. Most topic models rely on word co-occurrence for computing a topic, i.e., a weighted set of words that together represent a high-level s
emantic concept. In this paper, we propose a new light-weight Self-Supervised Neural Topic Model (SNTM) that learns a rich context by learning a topic representation jointly from three co-occurring words and a document that the triple originates from. Our experimental results indicate that our proposed neural topic model, SNTM, outperforms previously existing topic models in coherence metrics as well as document clustering accuracy. Moreover, apart from the topic coherence and clustering performance, the proposed neural topic model has a number of advantages, namely, being computationally efficient and easy to train.
Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic
modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
Abstract Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities
do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.
There is a shortage of high-quality corpora for South-Slavic languages. Such corpora are useful to computer scientists and researchers in social sciences and humanities alike, focusing on numerous linguistic, content analysis, and natural language pr
ocessing applications. This paper presents a workflow for mining Wikipedia content and processing it into linguistically-processed corpora, applied on the Bosnian, Bulgarian, Croatian, Macedonian, Serbian, Serbo-Croatian and Slovenian Wikipedia. We make the resulting seven corpora publicly available. We showcase these corpora by comparing the content of the underlying Wikipedias, our assumption being that the content of the Wikipedias reflects broadly the interests in various topics in these Balkan nations. We perform the content comparison by using topic modelling algorithms and various distribution comparisons. The results show that all Wikipedias are topically rather similar, with all of them covering art, culture, and literature, whereas they contain differences in geography, politics, history and science.
This paper presents multidimensional Social Opinion Mining on user-generated content gathered from newswires and social networking services in three different languages: English ---a high-resourced language, Maltese ---a low-resourced language, and M
altese-English ---a code-switched language. Multiple fine-tuned neural classification language models which cater for the i) English, Maltese and Maltese-English languages as well as ii) five different social opinion dimensions, namely subjectivity, sentiment polarity, emotion, irony and sarcasm, are presented. Results per classification model for each social opinion dimension are discussed.
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
