Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generat
ion and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the in- efficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages.
Code-mixing is a phenomenon of mixing words and phrases from two or more languages in a single utterance of speech and text. Due to the high linguistic diversity, code-mixing presents several challenges in evaluating standard natural language generat
ion (NLG) tasks. Various widely popular metrics perform poorly with the code-mixed NLG tasks. To address this challenge, we present a metric in- dependent evaluation pipeline MIPE that significantly improves the correlation between evaluation metrics and human judgments on the generated code-mixed text. As a use case, we demonstrate the performance of MIPE on the machine-generated Hinglish (code-mixing of Hindi and English languages) sentences from the HinGE corpus. We can extend the proposed evaluation strategy to other code-mixed language pairs, NLG tasks, and evaluation metrics with minimal to no effort.
Code-mixed language plays a crucial role in communication in multilingual societies. Though the recent growth of web users has greatly boosted the use of such mixed languages, the current generation of dialog systems is primarily monolingual. This in
crease in usage of code-mixed language has prompted dialog systems in a similar language. We present our work in Code-Mixed Dialog Generation, an unexplored task in code-mixed languages, generating utterances in code-mixed language rather than a single language that is more often just English. We present a new synthetic corpus in code-mix for dialogs, CM-DailyDialog, by converting an existing English-only dialog corpus to a mixed Hindi-English corpus. We then propose a baseline approach where we show the effectiveness of using mBART like multilingual sequence-to-sequence transformers for code-mixed dialog generation. Our best performing dialog models can conduct coherent conversations in Hindi-English mixed language as evaluated by human and automatic metrics setting new benchmarks for the Code-Mixed Dialog Generation task.
In this paper we work with a hope speech detection corpora that includes English, Tamil, and Malayalam datasets. We present a two phase mechanism to detect hope speech. In the first phase we build a classifier to identify the language of the text. In
the second phase, we build a classifier to detect hope speech, non hope speech, or not lang labels. Experimental results show that hope speech detection is challenging and there is scope for improvement.
In this study, a simulation of the MTR-22MW reactor and a study
of standard and mixed fuel combustion using the Codes GETERA
and MCNP5.
The Results of the simulation showed that the operation time of the
reactor in the case of standard fuel is 274 days and if the use of
mixed fuel is 135 days.
Arch length preservation, and maintenance of the leeway space in the mixed dentition can often provide an adequate space to resolve lower incisor crowding. Yet, the frequency of this occurrence is not known. To obtain this information, lingual arches
were placed in the mandibular arches of 107 consecutive mixed dentition patients with incisor crowding to preserve arch length and make the leeway space available to resolve the crowding. Arch length decreased by only 0.44mm, whereas the intercanine, interpremolar, and intermolar dimensions increased by0.72-2.27mm. There was an adequate space to resolve the crowding in 65 out of 107 patients(60%). If perfect arch length preservation occurred, there would bean adequate space to resolve the crowding in 73 out of 107 patients(68%)
Malocclusion is considered one of the most prevalent dental problems .This research aimed to study the prevalence of malocclusion states in the Syrian coast community.
The sample of study consists of 1200 patients in mixed dentition stage (569 male
s – 631 females) with mean age (8.65 ± 1.4 years for male, 8.64 ±1.36 years for females).
The study showed a prevalence of malocclusion in about 84.8% of the sample, with about 58.33% for vertical plane malocclusion and 84% for sagittal plane malocclusion, and 37.8% for transversal plane malocclusion.
We concluded that it is very important to improve the hygiene awareness between our community population for better prevention and treatment of these problems.
