JAVANESE AFFECTIVE WORDS IN TERM OF ADDRESS

One of language functions is to express someone’s feeling to others. This accomodates good and bad condition experienced when people are interacting with others. Good experiencesare usually represented by acceptable expression in all situation and people. On the otherhand, people also sometimes have to deal with condition in which they do not feel happy with. Language, then, is used to accomodate that bad experience in the form of usingdeictical expression, especially person deixis. Then, this deixis is classified as one type ofharse languages and apperars in the use of addressee system in language. Harse languageexpressing addressee system in Javanese language is practiced in daily life and in various scales of usage. The use and form of this addressee system differ from the standard one. Atleast, there are seven representations of addressee system in harse language, namelyreplacing person’s name by animal, by kind of occupation, by mentioning abnormal part ofbody, by words expressing retarded menta, by using racis or classis words, and by spiritualcreature. These addressee systems also indicate social functions. There are four functions ofaddressee, they are indicating respect to someone being addressed, showing solidarity among members of community, expressing inconvenient feelings, and insulting otherpersons

Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs

In this paper, we study the problem of addressee and response selection in multi-party conversations. Understanding multi-party conversations is challenging because of complex speaker interactions: multiple speakers exchange messages with each other, playing different roles (sender, addressee, observer), and these roles vary across turns. To tackle this challenge, we propose the Speaker Interaction Recurrent Neural Network (SI-RNN). Whereas the previous state-of-the-art system updated speaker embeddings only for the sender, SI-RNN uses a novel dialog encoder to update speaker embeddings in a role-sensitive way. Additionally, unlike the previous work that selected the addressee and response separately, SI-RNN selects them jointly by viewing the task as a sequence prediction problem. Experimental results show that SI-RNN significantly improves the accuracy of addressee and response selection, particularly in complex conversations with many speakers and responses to distant messages many turns in the past.Comment: AAAI 201

The Pragmatics of Person and Imperatives in Sign Language of the Netherlands

We present new evidence against a grammatical distinction between second and third person in Sign Language of The Netherlands (NGT). More precisely, we show how pushing this distinction into the domain of pragmatics helps account for an otherwise puzzling fact about the NGT imperative: not only is it used to command your addressee, it can also express ‘non-addressee-oriented commands’

The effect of perceptual availability and prior discourse on young children's use of referring expressions.

Choosing appropriate referring expressions requires assessing whether a referent is “available” to the addressee either perceptually or through discourse. In Study 1, we found that 3- and 4-year-olds, but not 2-year-olds, chose different referring expressions (noun vs. pronoun) depending on whether their addressee could see the intended referent or not. In Study 2, in more neutral discourse contexts than previous studies, we found that 3- and 4-year-olds clearly differed in their use of referring expressions according to whether their addressee had already mentioned a referent. Moreover, 2-yearolds responded with more naming constructions when the referent had not been mentioned previously. This suggests that, despite early social–cognitive developments, (a) it takes time tomaster the given/new contrast linguistically, and (b) children understand the contrast earlier based on discourse, rather than perceptual context

Addressee Identification In Face-to-Face Meetings

We present results on addressee identification in four-participants face-to-face meetings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act can be predicted based on gaze, utterance and conversational context features. Then, we explore whether information about meeting context can aid classifiers’ performances. Both classifiers perform the best when conversational context and utterance features are combined with speaker’s gaze information. The classifiers show little gain from information about meeting context

Speakers use their own discourse model to determine referents' accessibility during the production of referring expressions.

We report two experiments that investigated the widely-held assumption that speakers use the addressee's discourse model when choosing referring expressions, by manipulating whether the addressee could hear the immediately preceding linguistic context. Experiment 1 showed that speakers increased pronoun use (relative to definite NPs) when the referent was mentioned in the immediately preceding sentence compared to when it was not, but whether their addressee heard that the referent was mentioned had no effect, indicating that speakers use their own, privileged discourse model when choosing referring expressions. The same pattern of results was found in Experiment 2. Speakers produced fewer pronouns when the immediately preceding sentence mentioned a referential competitor than when it mentioned the referent, but this effect did not differ depending on whether the sentence was shared with their addressee. Thus, we conclude that choice of referring expression is determined by the referent's accessibility in the speaker’s own discourse model rather than the addressee's

Letter from Geraldine Ferraro to a Marymount College Classmate

Letter from Geraldine Ferraro to a fellow Marymount College classmate and graduate. Shares respective marriage anniversary news and wishes. Addressee resides in Brazil. Includes handwritten notes.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_personal/1002/thumbnail.jp

A comparison of addressee detection methods for multiparty conversations

Several algorithms have recently been proposed for recognizing addressees in a group conversational setting. These algorithms can rely on a variety of factors including previous conversational roles, gaze and type of dialogue act. Both statistical supervised machine learning algorithms as well as rule based methods have been developed. In this paper, we compare several algorithms developed for several different genres of muliparty dialogue, and propose a new synthesis algorithm that matches the performance of machine learning algorithms while maintaning the transparancy of semantically meaningfull rule-based algorithms

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing.Comment: Accepted by EMNLP 2023. arXiv admin note: text overlap with arXiv:2203.0850