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Found 9 papers, 5 papers with code
A Large-Scale Corpus for Conversation Disentanglement
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets.
Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems
Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e. g. flight booking, hotel reservation, technical support, student advising etc.
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
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.
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.
MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
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.
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.
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15. 5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention.
Ranked #43 on
Code Generation
on MBPP
