Question answering

Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen

2022

Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation

Conversational AI

Improving question-answering models that use data from tables

Patrick Ng

February 28, 2022

Novel pretraining method enables increases of 5% to 14% on five different evaluation metrics.

Search and information retrieval

Compositional task oriented parsing as abstractive question answering

Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, Claire Cardie

NAACL 2022

2022

Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to apply seq2seq models to generate linearized parse trees. A more recent line of work argues that pretrained seq2seq models are better at generating outputs that are themselves natural language, so they replace linearized parse trees

Conversational AI

Retrieval augmented classification for long-tail visual recognition

Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Chunhua Shen, Anton van den Hengel

CVPR 2022

2022

We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a standard base image encoder fused with a parallel retrieval branch that queries a non-parametric external memory of pre-encoded images and associated text snippets. We apply RAC to the problem of long-tail classification and

Computer vision

PerKGQA: Question answering over personalized knowledge graphs

Ritam Dutt, Kasturi Bhattacharjee, Rashmi Gangadharaiah, Dan Roth, Carolyn Penstein Rosé

NAACL 2022, ACL 2022 Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge

2022

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is leveraged similarly for all users’ queries during inference. However, such an assumption is not applicable to real-world settings, such as healthcare, where one needs to handle queries of new users over unseen KGs during inference. Furthermore

Conversational AI

Exploring the role of task transferability in large-scale multi-task learning

Vishakh Padmakumar, Leonard Lausen, Miguel Ballesteros, Sheng Zha, He He, George Karypis

NAACL 2022

2022

Recent work has found that multi-task training with a large number of diverse tasks can uniformly improve downstream performance on unseen target tasks. In contrast, literature on task transferability has established that the choice of intermediate tasks can heavily affect downstream task performance. In this work, we aim to disentangle the effect of scale and relatedness of tasks in multi-task representation

Machine learning

From rewriting to remembering: Common ground for conversational QA models

Marco Del Tredici, Xiaoyu Shen, Gianni Barlacchi, Bill Byrne, Adrià de Gispert

ACL 2022

2022

In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract relevant information as the conversation unwinds. We introduce the Common Ground (CG), an approach to accumulate conversational information as it emerges and select the relevant information at every turn. We show that CG offers a more

Conversational AI

semiPQA: A study on product question answering over semi-structured data

Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adrià de Gispert

ACL 2022 Workshop on ECNLP

2022

Product question answering (PQA) aims to automatically address customer questions to improve their online shopping experience. Current research mainly focuses on finding answers from either unstructured text, like product descriptions and user reviews, or structured knowledge bases with pre-defined schemas. Apart from the above two sources, a lot of product information is represented in a semistructured

Conversational AI

Product answer generation from heterogeneous sources: A new benchmark and best practices

Xiaoyu Shen, Gianni Barlacchi, Marco Del Tredici, Weiwei Cheng, Adrià de Gispert, Bill Byrne

ACL 2022 Workshop on ECNLP, NAACL 2022

2022

It is of great value to answer product questions based on heterogeneous information sources available on web product pages, e.g., semistructured attributes, text descriptions, user-provided contents, etc. However, these sources have different structures and writing styles, which poses challenges for (1) evidence ranking, (2) source selection, and (3) answer generation. In this paper, we build a benchmark

Conversational AI

Answer consolidation: Formulation and benchmarking

Wenxuan Zhou, Qiang Ning, Heba Elfardy, Kevin Small, Muhao Chen

NAACL 2022

2022

Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface. In this paper, we formulate the problem of answer consolidation

Conversational AI

Question answering

Work with us