Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Ranked #2 on Citation Intent Classification on ACL-ARC (using extra training data)
CITATION INTENT CLASSIFICATION CONVERSATIONAL RESPONSE SELECTION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS
This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.
For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
Ranked #1 on Text Classification on TREC-6
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information.
The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.
Ranked #1 on Conversational Response Selection on Advising Corpus
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
Ranked #2 on Conversational Response Selection on DSTC7 Ubuntu