71 research outputs found
Characterizing HCI Research in China: Streams, Methodologies and Future Directions
This position paper takes the first step to attempt to present the initial
characterization of HCI research in China. We discuss the current streams and
methodologies of Chinese HCI research based on two well-known HCI theories:
Micro/Marco-HCI and the Three Paradigms of HCI. We evaluate the discussion with
a survey of Chinese publications at CHI 2019, which shows HCI research in China
has less attention to Macro-HCI topics and the third paradigms of HCI
(Phenomenologically situated Interaction). We then propose future HCI research
directions such as paying more attention to Macro-HCI topics and third paradigm
of HCI, combining research methodologies from multiple HCI paradigms, including
emergent users who have less access to technology, and addressing the cultural
dimensions in order to provide better technical solutions and support
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
The automatic recognition of micro-expression has been boosted ever since the
successful introduction of deep learning approaches. As researchers working on
such topics are moving to learn from the nature of micro-expression, the
practice of using deep learning techniques has evolved from processing the
entire video clip of micro-expression to the recognition on apex frame. Using
the apex frame is able to get rid of redundant video frames, but the relevant
temporal evidence of micro-expression would be thereby left out. This paper
proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based
on spatial information from the apex frame as well as on temporal information
from the respective-adjacent frames. Through extensive experiments on three
benchmarks, we demonstrate the improvement achieved by learning such temporal
information. Specially, the model with such temporal information is more robust
in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201
Thread of Thought Unraveling Chaotic Contexts
Large Language Models (LLMs) have ushered in a transformative era in the
field of natural language processing, excelling in tasks related to text
comprehension and generation. Nevertheless, they encounter difficulties when
confronted with chaotic contexts (e.g., distractors rather than long irrelevant
context), leading to the inadvertent omission of certain details within the
chaotic context. In response to these challenges, we introduce the "Thread of
Thought" (ThoT) strategy, which draws inspiration from human cognitive
processes. ThoT systematically segments and analyzes extended contexts while
adeptly selecting pertinent information. This strategy serves as a versatile
"plug-and-play" module, seamlessly integrating with various LLMs and prompting
techniques. In the experiments, we utilize the PopQA and EntityQ datasets, as
well as a Multi-Turn Conversation Response dataset (MTCR) we collected, to
illustrate that ThoT significantly improves reasoning performance compared to
other prompting techniques.Comment: 11 pages, 7 figures, 5 table
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted
sparse representations in vocabulary space, has shown promising results with
high quality and low latency. Despite it deeply exploiting the
lexicon-representing capability of pre-trained language models, a crucial gap
remains between language modeling and lexicon-weighting retrieval -- the former
preferring certain or low-entropy words whereas the latter favoring pivot or
high-entropy words -- becoming the main barrier to lexicon-weighting
performance for large-scale retrieval. To bridge this gap, we propose a
brand-new pre-training framework, lexicon-bottlenecked masked autoencoder
(LexMAE), to learn importance-aware lexicon representations. Essentially, we
present a lexicon-bottlenecked module between a normal language modeling
encoder and a weakened decoder, where a continuous bag-of-words bottleneck is
constructed to learn a lexicon-importance distribution in an unsupervised
fashion. The pre-trained LexMAE is readily transferred to the lexicon-weighting
retrieval via fine-tuning. On the ad-hoc retrieval benchmark, MS-Marco, it
achieves 42.6% MRR@10 with 45.8 QPS for the passage dataset and 44.4% MRR@100
with 134.8 QPS for the document dataset, by a CPU machine. And LexMAE shows
state-of-the-art zero-shot transfer capability on BEIR benchmark with 12
datasets.Comment: Appeared at ICLR 202
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
Building an intelligent dialogue system with the ability to select a proper
response according to a multi-turn context is a great challenging task.
Existing studies focus on building a context-response matching model with
various neural architectures or PLMs and typically learning with a single
response prediction task. These approaches overlook many potential training
signals contained in dialogue data, which might be beneficial for context
understanding and produce better features for response prediction. Besides, the
response retrieved from existing dialogue systems supervised by the
conventional way still faces some critical challenges, including incoherence
and inconsistency. To address these issues, in this paper, we propose learning
a context-response matching model with auxiliary self-supervised tasks designed
for the dialogue data based on pre-trained language models. Specifically, we
introduce four self-supervised tasks including next session prediction,
utterance restoration, incoherence detection and consistency discrimination,
and jointly train the PLM-based response selection model with these auxiliary
tasks in a multi-task manner. By this means, the auxiliary tasks can guide the
learning of the matching model to achieve a better local optimum and select a
more proper response. Experiment results on two benchmarks indicate that the
proposed auxiliary self-supervised tasks bring significant improvement for
multi-turn response selection in retrieval-based dialogues, and our model
achieves new state-of-the-art results on both datasets.Comment: 10 page
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