Search Results for author:
Found 25 papers, 18 papers with code
Do Sentence Transformers Learn Quasi-Geospatial Concepts from General Text?
Sentence transformers are language models designed to perform semantic search.
Natural Language User Profiles for Transparent and Scrutable Recommendations
This lack of transparency not only limits user understanding of why certain items are suggested but also reduces the user's ability to easily scrutinize and edit their preferences.
Lexical Entrainment for Conversational Systems
To address this, we propose a new dataset, named MULTIWOZ-ENTR, and a measure for LE for conversational systems.
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
Prompt tuning (PT), where a small amount of trainable soft (continuous) prompt vectors is affixed to the input of language models (LM), has shown promising results across various tasks and models for parameter-efficient fine-tuning (PEFT).
Application of Zone Method based Physics-Informed Neural Networks in Reheating Furnaces
Foundation Industries (FIs) constitute glass, metals, cement, ceramics, bulk chemicals, paper, steel, etc.
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System
This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.
Rethink the Effectiveness of Text Data Augmentation: An Empirical Analysis
In recent years, language models (LMs) have made remarkable progress in advancing the field of natural language processing (NLP).
Self Contrastive Learning for Session-based Recommendation
Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations.
A Survey on Asking Clarification Questions Datasets in Conversational Systems
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation.
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
Task-oriented dialogue systems aim at providing users with task-specific services.
When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
We move towards this target with two sub-tasks, a classification task and a ranking task.
Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner
Language models (LMs) trained on vast quantities of unlabelled data have greatly advanced the field of natural language processing (NLP).
Evaluation Metrics for Measuring Bias in Search Engine Results
However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point.
Attention-based Ingredient Phrase Parser
As virtual personal assistants have now penetrated the consumer market, with products such as Siri and Alexa, the research community has produced several works on task-oriented dialogue tasks such as hotel booking, restaurant booking, and movie recommendation.
Learning to Execute Actions or Ask Clarification Questions
In this paper, we extend the Minecraft Corpus Dataset by annotating all builder utterances into eight types, including clarification questions, and propose a new builder agent model capable of determining when to ask or execute instructions.
StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts
Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset.
Ranked #1 on
Question Answering
on StepGame
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Towards More Accountable Search Engines: Online Evaluation of Representation Bias
Information availability affects people's behavior and perception of the world.
Subsequence Based Deep Active Learning for Named Entity Recognition
Active Learning (AL) has been successfully applied to Deep Learning in order to drastically reduce the amount of data required to achieve high performance.
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results.
A Multilinear Sampling Algorithm to Estimate Shapley Values
In this work, we propose a new sampling method based on a multilinear extension technique as applied in game theory.
A Baseline for Shapley Values in MLPs: from Missingness to Neutrality
Deep neural networks have gained momentum based on their accuracy, but their interpretability is often criticised.
Predicting Engagement in Video Lectures
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of identifying and recommending the most suitable educational materials for learners.
Self-Attentive Hawkes Processes
The proposed method adapts self-attention to fit the intensity function of Hawkes processes.
