55 research outputs found
Generalized residual vector quantization for large scale data
Vector quantization is an essential tool for tasks involving large scale
data, for example, large scale similarity search, which is crucial for
content-based information retrieval and analysis. In this paper, we propose a
novel vector quantization framework that iteratively minimizes quantization
error. First, we provide a detailed review on a relevant vector quantization
method named \textit{residual vector quantization} (RVQ). Next, we propose
\textit{generalized residual vector quantization} (GRVQ) to further improve
over RVQ. Many vector quantization methods can be viewed as the special cases
of our proposed framework. We evaluate GRVQ on several large scale benchmark
datasets for large scale search, classification and object retrieval. We
compared GRVQ with existing methods in detail. Extensive experiments
demonstrate our GRVQ framework substantially outperforms existing methods in
term of quantization accuracy and computation efficiency.Comment: published on International Conference on Multimedia and Expo 201
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation
Human reading comprehension often requires reasoning of event semantic
relations in narratives, represented by Event-centric Question-Answering (QA).
To address event-centric QA, we propose a novel QA model with contrastive
learning and invertible event transformation, call TranCLR. Our proposed model
utilizes an invertible transformation matrix to project semantic vectors of
events into a common event embedding space, trained with contrastive learning,
and thus naturally inject event semantic knowledge into mainstream QA
pipelines. The transformation matrix is fine-tuned with the annotated event
relation types between events that occurred in questions and those in answers,
using event-aware question vectors. Experimental results on the Event Semantic
Relation Reasoning (ESTER) dataset show significant improvements in both
generative and extractive settings compared to the existing strong baselines,
achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact
Match (EM) score under the multi-answer setting. Qualitative analysis reveals
the high quality of the generated answers by TranCLR, demonstrating the
feasibility of injecting event knowledge into QA model learning. Our code and
models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202
CHIME : Cross-passage hierarchical memory network for generative review question answering
We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module
MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation
We propose MemoChat, a pipeline for refining instructions that enables large
language models (LLMs) to effectively employ self-composed memos for
maintaining consistent long-range open-domain conversations. We demonstrate a
long-range open-domain conversation through iterative
"memorization-retrieval-response" cycles. This requires us to carefully design
tailored tuning instructions for each distinct stage. The instructions are
reconstructed from a collection of public datasets to teach the LLMs to
memorize and retrieve past dialogues with structured memos, leading to enhanced
consistency when participating in future conversations. We invite experts to
manually annotate a test set designed to evaluate the consistency of long-range
conversations questions. Experiments on three testing scenarios involving both
open-source and API-accessible chatbots at scale verify the efficacy of
MemoChat, which outperforms strong baselines.Comment: Codes, data and models will be available soo
Event-centric question answering via contrastive learning and invertible event transformation
Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR
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