Search Results for author:
Found 8 papers, 3 papers with code
EntSUM: A Data Set for Entity-Centric Extractive Summarization
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control. We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries.
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
In this paper, we propose MAML-en-LLM, a novel method for meta-training LLMs, which can learn truly generalizable parameters that not only perform well on disjointed tasks but also adapts to unseen tasks.
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning
Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time.
EntSUM: A Data Set for Entity-Centric Summarization
Our analysis and results show the challenging nature of this task and of the proposed data set.
Learning Rich Representation of Keyphrases from Text
In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8. 16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction.
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference
Named entity recognition is a key component of many text processing pipelines and it is thus essential for this component to be robust to different types of input.
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings.
Toward Data-Driven Tutorial Question Answering with Deep Learning Conversational Models
This paper reports on the creation of a dataset that could support building such a tutorial question answering system and discusses the methodology to create the 106, 386 question strong dataset.
