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Found 16 papers, 5 papers with code
On the Role of Corpus Ordering in Language Modeling
Language models pretrained on vast corpora of unstructured text using self-supervised learning framework are used in numerous natural language understanding and generation tasks.
Analyzing the Dialect Diversity in Multi-document Summaries
In this work, we take a complementary approach to analyzing and improving the quality of summaries generated from social media data in terms of their ability to represent salient as well as diverse perspectives.
What Drives Performance in Multilingual Language Models?
This study investigates the factors influencing the performance of multilingual large language models (MLLMs) across diverse languages.
ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remains underexplored.
Narrating Causal Graphs with Large Language Models
The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts.
Making a Long Story Short in Conversation Modeling
Conversation systems accommodate diverse users with unique personalities and distinct writing styles.
Predicting Evoked Emotions in Conversations
In this work, we introduce the novel problem of Predicting Emotions in Conversations (PEC) for the next turn (n+1), given combinations of textual and/or emotion input up to turn n. We systematically approach the problem by modeling three dimensions inherently connected to evoked emotions in dialogues, including (i) sequence modeling, (ii) self-dependency modeling, and (iii) recency modeling.
Generating Continuations in Multilingual Idiomatic Contexts
The ability to process idiomatic or literal multiword expressions is a crucial aspect of understanding and generating any language.
Exploring the Maze of Multilingual Modeling
Multilingual language models have gained significant attention in recent years, enabling the development of applications that meet diverse linguistic contexts.
Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples
Prior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but for the same task.
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks
Affective tasks such as sentiment analysis, emotion classification, and sarcasm detection have been popular in recent years due to an abundance of user-generated data, accurate computational linguistic models, and a broad range of relevant applications in various domains.
Learning Emotion-enriched Word Representations
As a consequence, emotionally dissimilar words, such as {``}happy{''} and {``}sad{''} occurring in similar contexts would purport more similar meaning than emotionally similar words, such as {``}happy{''} and {``}joy{''}.
Selective Co-occurrences for Word-Emotion Association
Emotion classification from text typically requires some degree of word-emotion association, either gathered from pre-existing emotion lexicons or calculated using some measure of semantic relatedness.
