200 citations · 811 across the 66 of their papers we have counts for
11 papers · 1 filter
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
Parishad BehnamGhader, Santiago Miret, Siva Reddy
Augmenting pretrained language models with retrievers has shown promise in effectively solving common NLP problems, such as language modeling and question answering. In this paper,…
Syntactic Substitutability as Unsupervised Dependency Syntax
Jasper Jian, Siva Reddy
Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. In this work, we explore the hypothesis that syntactic dependencies…
The Curious Case of Absolute Position Embeddings
Koustuv Sinha, Amirhossein Kazemnejad, Siva Reddy +3
Transformer language models encode the notion of word order using positional information. Most commonly, this positional information is represented by absolute position embeddings…
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems
Devang Kulshreshtha, Muhammad Shayan, Robert Belfer +3
Existing work on generating hints in Intelligent Tutoring Systems (ITS) focuses mostly on manual and non-personalized feedback. In this work, we explore automatically generated que…
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?
Nouha Dziri, Sivan Milton, Mo Yu +2
Knowledge-grounded conversational models are known to suffer from producing factually invalid statements, a phenomenon commonly called hallucination. In this work, we investigate t…
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Zichao Li, Prakhar Sharma, Xing Han Lu +2
Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA syst…
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue
Nouha Dziri, Ehsan Kamalloo, Sivan Milton +4
The goal of information-seeking dialogue is to respond to seeker queries with natural language utterances that are grounded on knowledge sources. However, dialogue systems often pr…
Image Retrieval from Contextual Descriptions
Benno Krojer, Vaibhav Adlakha, Vibhav Vineet +3
The ability to integrate context, including perceptual and temporal cues, plays a pivotal role in grounding the meaning of a linguistic utterance. In order to measure to what exten…
Combining Modular Skills in Multitask Learning
Edoardo M. Ponti, Alessandro Sordoni, Yoshua Bengio +1
A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that ea…
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Emanuele Bugliarello, Fangyu Liu, Jonas Pfeiffer +4
Reliable evaluation benchmarks designed for replicability and comprehensiveness have driven progress in machine learning. Due to the lack of a multilingual benchmark, however, visi…
Does Entity Abstraction Help Generative Transformers Reason?
Nicolas Gontier, Siva Reddy, Christopher Pal
We study the utility of incorporating entity type abstractions into pre-trained Transformers and test these methods on four NLP tasks requiring different forms of logical reasoning…