55 citations · 120 across the 24 of their papers we have counts for
26 papers · 1 filter
SaliMory: Orchestrating Cognitive Memory for Conversational Agents
Kai Zhang, Xinyuan Zhang, Hongda Jiang +11
Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context windows with raw retrieval degrad…
AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval
Kai Zhang, Xinyuan Zhang, Ejaz Ahmed +11
Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios w…
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints
Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin +7
Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to…
Unsupervised Melody-to-Lyric Generation
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby +8
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody. It is of significant practical interest and more challenging than uncon…
Context-Situated Pun Generation
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby +5
Previous work on pun generation commonly begins with a given pun word (a pair of homophones for heterographic pun generation and a polyseme for homographic pun generation) and seek…
ExPUNations: Augmenting Puns with Keywords and Explanations
Jiao Sun, Anjali Narayan-Chen, Shereen Oraby +5
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add t…
Style Control for Schema-Guided Natural Language Generation
Alicia Y. Tsai, Shereen Oraby, Vittorio Perera +5
Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are cruc…
Learning from Mistakes: Combining Ontologies via Self-Training for Dialogue Generation
Lena Reed, Vrindavan Harrison, Shereen Oraby +2
Natural language generators (NLGs) for task-oriented dialogue typically take a meaning representation (MR) as input. They are trained end-to-end with a corpus of MR/utterance pairs…
Schema-Guided Natural Language Generation
Yuheng Du, Shereen Oraby, Vittorio Perera +5
Neural network based approaches to data-to-text natural language generation (NLG) have gained popularity in recent years, with the goal of generating a natural language prompt that…
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast
Vrindavan Harrison, Lena Reed, Shereen Oraby +1
Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of d…
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG
Shereen Oraby, Vrindavan Harrison, Abteen Ebrahimi +1
Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years. While we have seen progress with generating synta…
Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?
Lena Reed, Shereen Oraby, Marilyn Walker
Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. Fo…
Neural MultiVoice Models for Expressing Novel Personalities in Dialog
Shereen Oraby, Lena Reed, Sharath TS +2
Natural language generators for task-oriented dialog should be able to vary the style of the output utterance while still effectively realizing the system dialog actions and their…
Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators
Shereen Oraby, Lena Reed, Shubhangi Tandon +3
Natural language generators for task-oriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable f…
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems
Kevin K. Bowden, Jiaqi Wu, Shereen Oraby +2
In dialogue systems, the tasks of named entity recognition (NER) and named entity linking (NEL) are vital preprocessing steps for understanding user intent, especially in open doma…
Exploring Conversational Language Generation for Rich Content about Hotels
Marilyn A. Walker, Albry Smither, Shereen Oraby +2
Dialogue systems for hotel and tourist information have typically simplified the richness of the domain, focusing system utterances on only a few selected attributes such as price,…
Slugbot: An Application of a Novel and Scalable Open Domain Socialbot Framework
Kevin K. Bowden, Jiaqi Wu, Shereen Oraby +2
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We…
Summarizing Dialogic Arguments from Social Media
Amita Misra, Shereen Oraby, Shubhangi Tandon +3
Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Com…
"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts
Shereen Oraby, Pritam Gundecha, Jalal Mahmud +2
Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purp…
Combining Search with Structured Data to Create a More Engaging User Experience in Open Domain Dialogue
Kevin K. Bowden, Shereen Oraby, Jiaqi Wu +2
The greatest challenges in building sophisticated open-domain conversational agents arise directly from the potential for ongoing mixed-initiative multi-turn dialogues, which do no…
Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue
Shereen Oraby, Vrindavan Harrison, Lena Reed +3
The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus…
Harvesting Creative Templates for Generating Stylistically Varied Restaurant Reviews
Shereen Oraby, Sheideh Homayon, Marilyn Walker
Many of the creative and figurative elements that make language exciting are lost in translation in current natural language generation engines. In this paper, we explore a method…
Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog
Shereen Oraby, Vrindavan Harrison, Amita Misra +2
Effective models of social dialog must understand a broad range of rhetorical and figurative devices. Rhetorical questions (RQs) are a type of figurative language whose aim is to a…
And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue
Shereen Oraby, Lena Reed, Ryan Compton +3
We investigate the characteristics of factual and emotional argumentation styles observed in online debates. Using an annotated set of "factual" and "feeling" debate forum posts, w…