Dialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. The ability to compare dialogs is beneficial in many rea
l world use cases, such as conversation analytics for contact center calls and virtual agent design. We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants. We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets. The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.
Content moderation is often performed by a collaboration between humans and machine learning models. However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system performance. This wo
rk presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process. First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions. Using these metrics, we conduct a large benchmark study evaluating the performance of state-of-the-art uncertainty models under different collaborative review strategies. We find that an uncertainty-based strategy consistently outperforms the widely used strategy based on toxicity scores, and moreover that the choice of review strategy drastically changes the overall system performance. Our results demonstrate the importance of rigorous metrics for understanding and developing effective moderator-model systems for content moderation, as well as the utility of uncertainty estimation in this domain.
The present paper deals with a computational analysis of translationese in professional and student English-to-German translations belonging to different registers. Building upon an information-theoretical approach, we test translation conformity to
source and target language in terms of a neural language model's perplexity over Part of Speech (PoS) sequences. Our primary focus is on register diversification vs. convergence, reflected in the use of constructions eliciting a higher vs. lower perplexity score. Our results show that, against our expectations, professional translations elicit higher perplexity scores from a target language model than students' translations. An analysis of the distribution of PoS patterns across registers shows that this apparent paradox is the effect of higher stylistic diversification and register sensitivity in professional translations. Our results contribute to the understanding of human translationese and shed light on the variation in texts generated by different translators, which is valuable for translation studies, multilingual language processing, and machine translation.
It has long been recognized that suffixing is more common than prefixing in the languages of the world. More detailed statistics on this tendency are needed to sharpen proposed explanations for this tendency. The classic approach to gathering data on
the prefix/suffix preference is for a human to read grammatical descriptions (948 languages), which is time-consuming and involves discretization judgments. In this paper we explore two machine-driven approaches for prefix and suffix statistics which are crude approximations, but have advantages in terms of time and replicability. The first simply searches a large collection of grammatical descriptions for occurrences of the terms prefix' and suffix' (4 287 languages). The second counts substrings from raw text data in a way indirectly reflecting prefixation and suffixation (1 030 languages, using New Testament translations). The three approaches largely agree in their measurements but there are important theoretical and practical differences. In all measurements, there is an overall preference for suffixation, albeit only slightly, at ratios ranging between 0.51 and 0.68.
We generalize the notion of measuring social biases in word embeddings to visually grounded word embeddings. Biases are present in grounded embeddings, and indeed seem to be equally or more significant than for ungrounded embeddings. This is despite
the fact that vision and language can suffer from different biases, which one might hope could attenuate the biases in both. Multiple ways exist to generalize metrics measuring bias in word embeddings to this new setting. We introduce the space of generalizations (Grounded-WEAT and Grounded-SEAT) and demonstrate that three generalizations answer different yet important questions about how biases, language, and vision interact. These metrics are used on a new dataset, the first for grounded bias, created by augmenting standard linguistic bias benchmarks with 10,228 images from COCO, Conceptual Captions, and Google Images. Dataset construction is challenging because vision datasets are themselves very biased. The presence of these biases in systems will begin to have real-world consequences as they are deployed, making carefully measuring bias and then mitigating it critical to building a fair society.
This research aims to study the effect of adding alloying elements and heat treatment
of Zinc metal on solar energy absorbing , nine alloys ingots were manufactured by
changing the percentages of added Aluminum and Copper on the pure Zinc, and thes
e
ratios of Aluminum were : (10% , 20% , 30% , 40 % , 50%) to demonstrate the effect of
adding Aluminum to Zinc metal on solar energy absorbing , and ratios of copper were :
(20% , 40%) , as well as we prepare two pure zinc samples with 99.2% of purity , one
was rapidly cooled and the other slowly cooled , to demonstrate the effect of heat treatment
on solar energy absorbing .
In order to measure the solar energy absorbing for prepared samples , we
manufactured a device depends on the methods of heat exchange between solar radiation
and the surface exposed to radiation .
The obtained results showed that adding Aluminum and Copper to the pure Zinc
caused a decrease in solar energy absorbing .
As well as increasing the percentages of adding Aluminum and Copper to the pure
Zinc caused a gradually decrease in solar energy absorbing .
comparing the absorbing of pure zinc samples, one was rapidly cooled and the other
slowly cooled , the results showed that the sample was rapidly cooled was better than the
sample slowly cooled on solar energy absorbing .
This research aimed at analyzing the factorial structure of
Tennesse Standard of the self-concept (the edition of adults) after
applying it on two samples from Tishreen university students
(applied faculties- theoretical faculties).
The aim of the research is to measuring the employees trends and direction toward a
new way of adjusting the performance instead of the extent to which, as well as the
extent to which this new way could possibility depend on instead of the existing
way
now.
In order to achieve the adjectives of the research study a questionnaire of (56)
questions components has been designed covering the main subjects and fields of the
research variables, the number of individuals of this random sample examined
research (174). Besides, the resaerchen used the study method of the descriptive
analyzing study where be mode a comprehensive survey to all responses acquired
end in the meantime used the (SPSS.V 15) program for analyzing and dealing with
the inquiries concerned.
