Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog
تقول صورة متحركة ألف كلمة على الأقل: تحديد ردود GIF المستندة إلى GIF في مربع حوار MultiModal
126
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
المحادثات عبر الإنترنت تشمل أكثر من مجرد نص.على نحو متزايد، تعمل الاستجابات القائمة على الصور مثل الميمات وحلويات الرسوم المتحركة استجابات معترف بها ثقافيا وغالبا ما تكون روح الدعابة في المحادثة.ومع ذلك، في حين أن NLP تم توسيعها إلى نماذج متعددة الوسائط، فإن أنظمة حوار المحادثة تركز إلى حد كبير على توليد الردود النصية فقط.هنا، نقدم مجموعة بيانات جديدة تبلغ 1.56 مليون محادثة Text-GIF تتحول وإدخال نموذج محادثة متعددة الوسائط جديد Pepe جمبري الملك لتحديد الردود القائمة على GIF.نوضح أن نموذجنا ينتج استجابات GIF ذات الصلة وعالية الجودة، وفي تجربة مراقبة عشوائية كبيرة ترد على المستخدمين الحقيقيين، نظين على أن ردودنا النموذجية لدينا مع صور متحركة يتم استلامها بشكل أفضل من قبل المجتمع.
Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community.
References used
https://aclanthology.org/
rate research
Read More
Speaker gestures are semantically co-expressive with speech and serve different pragmatic functions to accompany oral modality. Therefore, gestures are an inseparable part of the language system: they may add clarity to discourse, can be employed to
facilitate lexical retrieval and retain a turn in conversations, assist in verbalizing semantic content and facilitate speakers in coming up with the words they intend to say. This aspect is particularly relevant in political discourse, where speakers try to apply communication strategies that are both clear and persuasive using verbal and non-verbal cues. In this paper we investigate the co-speech gestures of several Italian politicians during face-to-face interviews using a multimodal linguistic approach. We first enrich an existing corpus with a novel annotation layer capturing the function of hand movements. Then, we perform an analysis of the corpus, focusing in particular on the relationship between hand movements and other information layers such as the political party or non-lexical and semi-lexical tags. We observe that the recorded differences pertain more to single politicians than to the party they belong to, and that hand movements tend to occur frequently with semi-lexical phenomena, supporting the lexical retrieval hypothesis.
Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that maps embeddings to a common space. Alternatively, word embeddings may
be understood as nodes in a weighted graph. This framing allows us to examine a node's graph neighborhood without assuming a linear transform, and exploits new techniques from the graph matching optimization literature. These contrasting approaches have not been compared in BLI so far. In this work, we study the behavior of Euclidean versus graph-based approaches to BLI under differing data conditions and show that they complement each other when combined. We release our code at https://github.com/kellymarchisio/euc-v-graph-bli.
Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction. Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structu
re are still largely elusive and widely debated amongst linguists. This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpus-based approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB). The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.
We focus on dialog models in the context of clinical studies where the goal is to help gather, in addition to the close information collected based on a questionnaire, serendipitous information that is medically relevant. To promote user engagement a
nd address this dual goal (collecting both a predefined set of data points and more informal information about the state of the patients), we introduce an ensemble model made of three bots: a task-based, a follow-up and a social bot. We introduce a generic method for developing follow-up bots. We compare different ensemble configurations and we show that the combination of the three bots (i) provides a better basis for collecting information than just the information seeking bot and (ii) collects information in a more user-friendly, more efficient manner that an ensemble model combining the information seeking and the social bot.
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that conta
in rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
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
