Search Results for author:
Found 22 papers, 2 papers with code
SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)
Divided into 13 tracks, the task focused on methods to identify complex named entities (like names of movies, products and groups) in 11 languages in both monolingual and multi-lingual scenarios.
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings
Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks.
Dynamic Gazetteer Integration in Multilingual Models for Cross-Lingual and Cross-Domain Named Entity Recognition
Named entity recognition (NER) in a real-world setting remains challenging and is impacted by factors like text genre, corpus quality, and data availability.
Cross-Domain Named Entity Recognition
Cross-Lingual Transfer
+4
Evaluation Metrics of Language Generation Models for Synthetic Traffic Generation Tasks
Experiments on three tasks, i. e., Shopping Utterance Generation, Product Question Generation and Query Auto Completion, demonstrate that our metrics are effective for evaluating STG tasks, and improve the agreement with human judgement up to 20% with respect to common NLG metrics.
Follow-on Question Suggestion via Voice Hints for Voice Assistants
Using a new dataset of 6681 input questions and human written hints, we evaluated the models with automatic metrics and human evaluation.
InstructPTS: Instruction-Tuning LLMs for Product Title Summarization
E-commerce product catalogs contain billions of items.
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings.
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search
We propose an intent-aware FAQ retrieval system consisting of (1) an intent classifier that predicts when a user's information need can be answered by an FAQ; (2) a reformulation model that rewrites a query into a natural question.
Answering Unanswered Questions through Semantic Reformulations in Spoken QA
Spoken Question Answering (QA) is a key feature of voice assistants, usually backed by multiple QA systems.
Faithful Low-Resource Data-to-Text Generation through Cycle Training
Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text.
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)
The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Preventing Catastrophic Forgetting in Continual Learning of New Natural Language Tasks
In this paper, we approach the problem of incrementally expanding MTL models' capability to solve new tasks over time by distilling the knowledge of an already trained model on n tasks into a new one for solving n+1 tasks.
Reinforced Question Rewriting for Conversational Question Answering
Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.
Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance
Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge.
MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition
We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets.
You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions
We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user{'}s sentiment towards an entity from the conversation context, and 2) transforms the ratings of {``}similar{''} external reviewers to predict the current user{'}s preferences.
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input
We propose GEMNET, a novel approach for gazetteer knowledge integration, including (1) a flexible Contextual Gazetteer Representation (CGR) encoder that can be fused with any word-level model; and (2) a Mixture-of- Experts gating network that overcomes the feature overuse issue by learning to conditionally combine the context and gazetteer features, instead of assigning them fixed weights.
VoiSeR: A New Benchmark for Voice-Based Search Refinement
This common user intent is usually available through a {``}filter-by{''} interface on online shopping websites, but is challenging to support naturally via voice, as the intent of refinements must be interpreted in the context of the original search, the initial results, and the available product catalogue facets.
Research Challenges in Building a Voice-based Artificial Personal Shopper - Position Paper
Recent advances in automatic speech recognition lead toward enabling a voice conversation between a human user and an intelligent virtual assistant.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient
Learning a goal-oriented dialog policy is generally performed offline with supervised learning algorithms or online with reinforcement learning (RL).
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
It is well known that collaborative filtering (CF) based recommender systems provide better modeling of users and items associated with considerable rating history.
