Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOptimizing a Supervised Classifier for a Difficult Language Identification Problem
تحسين مصنف إشراف لمشكلة تحديد اللغة الصعبة
193
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
توضح هذه الورقة النظام الذي طورته STATITATIAIRE D'Analyze StatistIck Des Tyses لتحديد الهوية الحالية (DLI) المهمة المشتركة لعام 2021. هذه المهمة صعبة للغاية لأن المواد تتكون من تعليقات YouTube قصيرة، مكتوبة في البرنامج النصي الروماني، من ثلاثةلغات Dravidian ذات الصلة ارتباطا وثيقا، وفئة رابعة تتكون من العديد من اللغات الأخرى في أبعاد متفاوتة، كلها مختلطة مع اللغة الإنجليزية.يتكون النظام المقترح من نموذج الانحدار اللوجستي الذي يستخدمه كلما يتميز فقط N-Grams من الشخصيات بحد أقصى طول 5. بعد تحسينها من حيث ترجيح الميزة ومعلمات المصنف، فهو المرتبة الأولى في التحدي.تقوم التحليلات الإضافية التي أجريت تسطير أهمية التحسين، خاصة عندما يكون مقياس الفعالية هو الماكرو F1.
This paper describes the system developed by the Laboratoire d'analyse statistique des textes for the Dravidian Language Identification (DLI) shared task of VarDial 2021. This task is particularly difficult because the materials consists of short YouTube comments, written in Roman script, from three closely related Dravidian languages, and a fourth category consisting of several other languages in varying proportions, all mixed with English. The proposed system is made up of a logistic regression model which uses as only features n-grams of characters with a maximum length of 5. After its optimization both in terms of the feature weighting and the classifier parameters, it ranked first in the challenge. The additional analyses carried out underline the importance of optimization, especially when the measure of effectiveness is the Macro-F1.
References used
https://aclanthology.org/
rate research
Read More
This paper presents a technique for the identification of participant slots in English language contracts. Taking inspiration from unsupervised slot extraction techniques, the system presented here uses a supervised approach to identify terms used to
refer to a genre-specific slot in novel contracts. We evaluate the system in multiple feature configurations to demonstrate that the best performing system in both genres of contracts omits the exact mention form from consideration---even though such mention forms are often the name of the slot under consideration---and is instead based solely on the dependency label and parent; in other words, a more reliable quantification of a party's role in a contract is found in what they do rather than what they are named.
Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence. Despite recent achievements in both tasks, there is still room for improvement due to the scarcity of labeled data.
To solve the problem, we propose a language model-based generative classifier (LMGC) for using more information from labels by treating the labels as an input while enhancing label representations by embedding descriptions for each label. Moreover, since this enables LMGC to make ready the representations for labels, unseen in the pre-training step, we can effectively use a pre-trained language model in LMGC. Experimental results on the RST-DT dataset show that our LMGC achieved the state-of-the-art F1 score of 96.72 in discourse segmentation. It further achieved the state-of-the-art relation F1 scores of 84.69 with gold EDU boundaries and 81.18 with automatically segmented boundaries, respectively, in sentence-level discourse parsing.
We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability
the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText-103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.
This paper describes the model built for the SIGTYP 2021 Shared Task aimed at identifying 18 typologically different languages from speech recordings. Mel-frequency cepstral coefficients derived from audio files are transformed into spectrograms, whi
ch are then fed into a ResNet-50-based CNN architecture. The final model achieved validation and test accuracies of 0.73 and 0.53, respectively.
Learning a good latent representation is essential for text style transfer, which generates a new sentence by changing the attributes of a given sentence while preserving its content. Most previous works adopt disentangled latent representation learn
ing to realize style transfer. We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style. Moreover, our algorithm for style transfer applies to both single-attribute and multi-attribute transfer. Extensive experimental results show that our method generally outperforms state-of-the-art approaches.
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
