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Found 60 papers, 36 papers with code
Lexical Repetitions Lead to Rote Learning: Unveiling the Impact of Lexical Overlap in Train and Test Reference Summaries
Ideal summarization models should generalize to novel summary-worthy content without remembering reference training summaries by rote.
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
Our study reveals that instruction controllable text summarization remains a challenging task for LLMs, since (1) all LLMs evaluated still make factual and other types of errors in their summaries; (2) all LLM-based evaluation methods cannot achieve a strong alignment with human annotators when judging the quality of candidate summaries; (3) different LLMs show large performance gaps in summary generation and evaluation.
Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment
The interactive nature of Large Language Models (LLMs) theoretically allows models to refine and improve their answers, yet systematic analysis of the multi-turn behavior of LLMs remains limited.
Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems
Making big purchases requires consumers to research or consult a salesperson to gain domain expertise.
Beyond the Chat: Executable and Verifiable Text-Editing with LLMs
Conversational interfaces powered by Large Language Models (LLMs) have recently become a popular way to obtain feedback during document editing.
Art or Artifice? Large Language Models and the False Promise of Creativity
Inspired by the Torrance Test of Creative Thinking (TTCT), which measures creativity as a process, we use the Consensual Assessment Technique [3] and propose the Torrance Test of Creative Writing (TTCW) to evaluate creativity as a product.
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles
In this paper, we propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
Next Steps for Human-Centered Generative AI: A Technical Perspective
Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI).
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context.
LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond
To address this, we propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation
Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics.
Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions
In a second usability study, we developed and implemented a reading exercise with 95 novice news readers to measure exposure to coverage diversity.
Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization
In long document controllable summarization, where labeled data is scarce, pretrained models struggle to adapt to the task and effectively respond to user queries.
Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation
Human evaluation is the foundation upon which the evaluation of both summarization systems and automatic metrics rests.
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed.
Discord Questions: A Computational Approach To Diversity Analysis in News Coverage
There are many potential benefits to news readers accessing diverse sources.
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification.
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning
Through this way, SESoM inherits the superior generalization of model ensemble approaches and simultaneously captures the sample-specific competence of each source prompt.
Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets
Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model's output over another is often necessary.
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap.
Structure Extraction in Task-Oriented Dialogues with Slot Clustering
Extracting structure information from dialogue data can help us better understand user and system behaviors.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Factual consistency is an essential quality of text summarization models in practical settings.
Exploring Neural Models for Query-Focused Summarization
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
DialFact: A Benchmark for Fact-Checking in Dialogue
Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation.
MixQG: Neural Question Generation with Mixed Answer Types
Asking good questions is an essential ability for both human and machine intelligence.
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Then, we fine-tune a base summarization model, which is trained on all training samples, on the clean (noisy) subset to obtain an \textit{expert} (\textit{anti-expert}) model.
Controllable Abstractive Dialogue Summarization with Sketch Supervision
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control.
QAConv: Question Answering on Informative Conversations
This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source.
Open-Retrieval Conversational Machine Reading
On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin.
CorDial: Coarse-to-fine Abstractive Dialogue Summarization with Controllable Granularity
2) A simple strategy to control the granularity of the final summary.
Improving Limited Labeled Dialogue State Tracking with Self-Supervision
We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior.
Probing Task-Oriented Dialogue Representation from Language Models
This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill.
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data.
Ranked #8 on
Semantic Parsing
on spider
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions given a knowledge base text which may require asking clarification questions.
A Simple Language Model for Task-Oriented Dialogue
Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response.
Ranked #2 on
Response Generation
on MMConv
TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue
The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.
Attention over Parameters for Dialogue Systems
Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans.
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Dialog state tracking (DST) is a core component in task-oriented dialog systems.
Ranked #4 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
dialog state tracking
Multi-domain Dialogue State Tracking
+1
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences
Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Sensational headlines are headlines that capture people's attention and generate reader interest.
Getting To Know You: User Attribute Extraction from Dialogues
User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated.
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
Learning to Memorize in Neural Task-Oriented Dialogue Systems
Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
Global-to-local Memory Pointer Networks for Task-Oriented Dialogue
In our model, a global memory encoder and a local memory decoder are proposed to share external knowledge.
Ranked #4 on
Task-Oriented Dialogue Systems
on KVRET
Towards End-to-end Automatic Code-Switching Speech Recognition
Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as "one" in English and "w\`an" in Chinese.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
Building large-scale datasets for training code-switching language models is challenging and very expensive.
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases.
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors.
Ranked #28 on
Sentiment Analysis
on SST-5 Fine-grained classification
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
Lack of text data has been the major issue on code-switching language modeling.
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition
We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data.
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases.
Ranked #10 on
Task-Oriented Dialogue Systems
on KVRET
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition
Zara, or {`}Zara the Supergirl{'} is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module.
Towards Empathetic Human-Robot Interactions
Although research on empathetic robots is still in the early stage, we described our approach using signal processing techniques, sentiment analysis and machine learning algorithms to make robots that can "understand" human emotion.
