Search Results for author:
Found 14 papers, 5 papers with code
DialDoc 2022 Shared Task: Open-Book Document-grounded Dialogue Modeling
The paper presents the results of the Shared Task hosted by the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering co-located at ACL 2022.
Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations
We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context
The integration of external personalized context information into document-grounded conversational systems has significant potential business value, but has not been well-studied.
Semi-Structured Object Sequence Encoders
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences.
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available.
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts.
Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.
doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.
Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1
This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1-degree horizontal resolution.
Atmospheric and Oceanic Physics
Multi-task Learning with Multi-head Attention for Multi-choice Reading Comprehension
Multiple-choice Machine Reading Comprehension (MRC) is an important and challenging Natural Language Understanding (NLU) task, in which a machine must choose the answer to a question from a set of choices, with the question placed in context of text passages or dialog.
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs.
Ranked #24 on
AMR Parsing
on LDC2017T10
IBM Research at the CoNLL 2018 Shared Task on Multilingual Parsing
This paper presents the IBM Research AI submission to the CoNLL 2018 Shared Task on Parsing Universal Dependencies.
