QuAC: Question Answering in Context

@inproceedings{Choi2018QuACQA,
  title={QuAC: Question Answering in Context},
  author={Eunsol Choi and He He and Mohit Iyyer and Mark Yatskar and Wen-tau Yih and Yejin Choi and Percy Liang and Luke Zettlemoyer},
  booktitle={EMNLP},
  year={2018}
}
We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total. [] Key Result Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at this http URL

Figures and Tables from this paper

CoQA: A Conversational Question Answering Challenge
TLDR
CoQA is introduced, a novel dataset for building Conversational Question Answering systems and it is shown that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning).
Conversational QA for FAQs
TLDR
The dataset and experiments show that it is possible to access domain specific FAQs with high quality using conversational QA systems with little training data, thanks to transfer learning, and results of state-of-the-art models including transfer learning from Wikipedia QA datasets to the authors' cooking FAQ dataset.
Conversational Question Answering: A Survey
TLDR
A comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers from 2016-2021 shows that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives.
SituatedQA: Incorporating Extra-Linguistic Contexts into QA
TLDR
This study introduces SituatedQA, an open-retrieval QA dataset where systems must produce the correct answer to a question given the temporal or geographical context, and shows that existing models struggle with producing answers that are frequently updated or from uncommon locations.
Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks
TLDR
QuAIL is presented, the first RC dataset to combine text-based, world knowledge and unanswerable questions, and to provide question type annotation that would enable diagnostics of the reasoning strategies by a given QA system.
BERT-CoQAC: BERT-Based Conversational Question Answering in Context
TLDR
This paper introduces a framework based on publicly available pre-trained language model called BERT for incorporating history turns into the system and proposes a history selection mechanism that selects the turns that are relevant and contributes the most to answer the current question.
Ask to Learn: A Study on Curiosity-driven Question Generation
TLDR
This work proposes a novel text generation task, namely Curiosity-driven Question Generation, tackling the problem of generating a question given the text that contains its answer, and investigates several automated metrics to measure the different properties of Curious Questions.
ELI5: Long Form Question Answering
TLDR
This work introduces the first large-scale corpus for long form question answering, a task requiring elaborate and in-depth answers to open-ended questions, and shows that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline.
An Empirical Study of Content Understanding in Conversational Question Answering
TLDR
The experimental results indicate some potential hazards in the benchmark datasets, QuAC and CoQA, for conversational comprehension research, and sheds light on both what models may learn and how datasets may bias the models.
Weakly-Supervised Open-Retrieval Conversational Question Answering
TLDR
This work introduces a learned weak supervision approach that can identify a paraphrased span of the known answer in a passage of a passage in the open-retrieval ConvQA setting under a weak supervision setting.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 32 REFERENCES
CoQA: A Conversational Question Answering Challenge
TLDR
CoQA is introduced, a novel dataset for building Conversational Question Answering systems and it is shown that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning).
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
TLDR
The task of Complex Sequential QA is introduced which combines the two tasks of answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and learning to converse through a series of coherently linked QA pairs.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
TLDR
A strong logistic regression model is built, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%).
Search-based Neural Structured Learning for Sequential Question Answering
TLDR
This work proposes a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search that effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
TLDR
It is shown that, in comparison to other recently introduced large-scale datasets, TriviaQA has relatively complex, compositional questions, has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and requires more cross sentence reasoning to find answers.
Know What You Don’t Know: Unanswerable Questions for SQuAD
TLDR
SQuadRUn is a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
The Web as a Knowledge-Base for Answering Complex Questions
TLDR
This paper proposes to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers, and empirically demonstrates that question decomposition improves performance from 20.8 precision@1 to 27.5 precision @1 on this new dataset.
Interpretation of Natural Language Rules in Conversational Machine Reading
TLDR
This paper formalise this task and develops a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios to assess its difficulty by evaluating the performance of rule-based and machine-learning baselines.
The NarrativeQA Reading Comprehension Challenge
TLDR
A new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts are presented, designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience.
NewsQA: A Machine Comprehension Dataset
TLDR
NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs, is presented and analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment.
...
1
2
3
4
...