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Found 32 papers, 12 papers with code
The Yahoo Query Treebank, V. 1.0
A description and annotation guidelines for the Yahoo Webscope release of Query Treebank, Version 1. 0, May 2016.
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.
Audio De-identification: A New Entity Recognition Task
To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy
We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available.
Audio De-identification - a New Entity Recognition Task
To this end, we define the task of audio de-ID, in which audio spans with entity mentions should be detected.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Semantically Driven Sentence Fusion: Modeling and Evaluation
Sentence fusion is the task of joining related sentences into coherent text.
What's the best place for an AI conference, Vancouver or ______: Why completing comparative questions is difficult
Here we study using such LMs to fill in entities in human-authored comparative questions, like ``Which country is older, India or ______?''
$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
TRUE: Re-evaluating Factual Consistency Evaluation
Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability.
All You May Need for VQA are Image Captions
Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation.
A Dataset for Sentence Retrieval for Open-Ended Dialogues
We report the performance of several retrieval baselines, including neural retrieval models, over the dataset.
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method
We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings.
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning
Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge.
MaXM: Towards Multilingual Visual Question Answering
In this paper, we propose scalable solutions to multilingual visual question answering (mVQA), on both data and modeling fronts.
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering
Question answering models commonly have access to two sources of "knowledge" during inference time: (1) parametric knowledge - the factual knowledge encoded in the model weights, and (2) contextual knowledge - external knowledge (e. g., a Wikipedia passage) given to the model to generate a grounded answer.
Multilingual Sequence-to-Sequence Models for Hebrew NLP
We demonstrate that by casting tasks in the Hebrew NLP pipeline as text-to-text tasks, we can leverage powerful multilingual, pretrained sequence-to-sequence models as mT5, eliminating the need for a specialized, morpheme-based, separately fine-tuned decoder.
What You See is What You Read? Improving Text-Image Alignment Evaluation
Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks.
Ranked #11 on
Visual Reasoning
on Winoground
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries.
Transferring Visual Attributes from Natural Language to Verified Image Generation
In this paper, we propose to strengthen the consistency property of T2I methods in the presence of natural complex language, which often breaks the limits of T2I methods by including non-visual information, and textual elements that require knowledge for accurate generation.
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input.
Abstractive Text Summarization
Natural Language Inference
+2
VideoCon: Robust Video-Language Alignment via Contrast Captions
Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions.
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment.
Multilingual Instruction Tuning With Just a Pinch of Multilinguality
As instruction-tuned large language models (LLMs) gain global adoption, their ability to follow instructions in multiple languages becomes increasingly crucial.
Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance
Despite it being the cornerstone of BPE, the most common tokenization algorithm, the importance of compression in the tokenization process is still unclear.
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs
In this work, we first introduce an approach for constructing datasets based on the model knowledge for detection and intervention methods in closed-book and open-book question-answering settings.
Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.
Abstractive Text Summarization
Natural Language Inference
+3
