5 research outputs found
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
We propose a neuralized undirected graphical model called Neural-Hidden-CRF
to solve the weakly-supervised sequence labeling problem. Under the umbrella of
probabilistic undirected graph theory, the proposed Neural-Hidden-CRF embedded
with a hidden CRF layer models the variables of word sequence, latent ground
truth sequence, and weak label sequence with the global perspective that
undirected graphical models particularly enjoy. In Neural-Hidden-CRF, we can
capitalize on the powerful language model BERT or other deep models to provide
rich contextual semantic knowledge to the latent ground truth sequence, and use
the hidden CRF layer to capture the internal label dependencies.
Neural-Hidden-CRF is conceptually simple and empirically powerful. It obtains
new state-of-the-art results on one crowdsourcing benchmark and three
weak-supervision benchmarks, including outperforming the recent advanced model
CHMM by 2.80 F1 points and 2.23 F1 points in average generalization and
inference performance, respectively.Comment: 13 pages, 4 figures, accepted by SIGKDD-202
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders
Pre-trained sentence representations are crucial for identifying significant
sentences in unsupervised document extractive summarization. However, the
traditional two-step paradigm of pre-training and sentence-ranking, creates a
gap due to differing optimization objectives. To address this issue, we argue
that utilizing pre-trained embeddings derived from a process specifically
designed to optimize cohensive and distinctive sentence representations helps
rank significant sentences. To do so, we propose a novel graph pre-training
auto-encoder to obtain sentence embeddings by explicitly modelling
intra-sentential distinctive features and inter-sentential cohesive features
through sentence-word bipartite graphs. These pre-trained sentence
representations are then utilized in a graph-based ranking algorithm for
unsupervised summarization. Our method produces predominant performance for
unsupervised summarization frameworks by providing summary-worthy sentence
representations. It surpasses heavy BERT- or RoBERTa-based sentence
representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural
Language Processing (EMNLP 2023
Automated Timeline Length Selection for Flexible Timeline Summarization
By producing summaries for long-running events, timeline summarization (TLS)
underpins many information retrieval tasks. Successful TLS requires identifying
an appropriate set of key dates (the timeline length) to cover. However, doing
so is challenging as the right length can change from one topic to another.
Existing TLS solutions either rely on an event-agnostic fixed length or an
expert-supplied setting. Neither of the strategies is desired for real-life TLS
scenarios. A fixed, event-agnostic setting ignores the diversity of events and
their development and hence can lead to low-quality TLS. Relying on
expert-crafted settings is neither scalable nor sustainable for processing many
dynamically changing events. This paper presents a better TLS approach for
automatically and dynamically determining the TLS timeline length. We achieve
this by employing the established elbow method from the machine learning
community to automatically find the minimum number of dates within the time
series to generate concise and informative summaries. We applied our approach
to four TLS datasets of English and Chinese and compared them against three
prior methods. Experimental results show that our approach delivers comparable
or even better summaries over state-of-art TLS methods, but it achieves this
without expert involvement