Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWhat is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP
ما هو تقييم Semeval؟تحليل منهجي لحملات التقييم في NLP
387
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
Semeval هو المكان الرئيسي في مجتمع NLP لاقتراح التحديات الجديدة والتقييم التجريبي المنهجي لأنظمة NLP.توفر هذه الورقة تحليلا قياسيا منهيا لسيميفال تهدف إلى الأدلة على أنماط المساهمات وراء Semeval.من خلال فهم توزيع أنواع المهام والمقاييس والبنية والمشاركة والاقتباسات مع مرور الوقت نهدف إلى الإجابة على السؤال حول ما يجري تقييمه من قبل Semeval.
SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.
References used
https://aclanthology.org/
rate research
Read More
The last years have shown rapid developments in the field of multimodal machine learning, combining e.g., vision, text or speech. In this position paper we explain how the field uses outdated definitions of multimodality that prove unfit for the mach
ine learning era. We propose a new task-relative definition of (multi)modality in the context of multimodal machine learning that focuses on representations and information that are relevant for a given machine learning task. With our new definition of multimodality we aim to provide a missing foundation for multimodal research, an important component of language grounding and a crucial milestone towards NLU.
Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language. Nowadays, with the use of large amounts of data gathered from social media, other varieties and registers need to be proce
ssed, which may present other challenges and difficulties. In this work, we focus on English and we present a preliminary analysis by comparing the TwitterAAE corpus, which is annotated for ethnicity, and WordNet by quantifying and explaining the online language that WordNet misses.
From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performa
nce on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.
The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents. This paper represents the second-place method for the task, an ensemble
of two approaches. While one approach relies on combining different embedding methods to extract diverse semantic and syntactic representations of words in context; the other utilizes extra data with a slightly customized Self-training, a semi-supervised learning technique, for sequence tagging problems. Both of our architectures take advantage of a strong language model, which was fine-tuned on a toxic classification task. Although experimental evidence indicates higher effectiveness of the first approach than the second one, combining them leads to our best results of 70.77 F1-score on the test dataset.
The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various
dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse 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
