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Wai Lam

45 papers hereh-index 5010.6k citations311 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author23
  • last author20

Across the 45 of 45 papers where every author was matched, so the position is known.

fields
  • cs.CL41
  • cs.AI2
  • cs.IR2
same name
  • Wai Lam — 34 papers, h 22
  • Wai Lam — 11 papers, h 3
  • Wai Lam — 7 papers, h 7
  • Wai Lam — 5 papers, h 4
  • Wai Lam — 5 papers, h 4
  • Wai Lam — 5 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations · 863 across the 45 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CL2017★ 3 cited

Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

Piji Li, Lidong Bing, Wai Lam

We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encode…

cs.CL2017★ 33 cited

Deep Recurrent Generative Decoder for Abstractive Text Summarization

Piji Li, Wai Lam, Lidong Bing +1

We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN…

cs.CL2017★ 306 cited

Neural Rating Regression with Abstractive Tips Generation for Recommendation

Piji Li, Zihao Wang, Zhaochun Ren +2

Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short te…

cs.CL2017★ 77 cited

Jointly Learning Word Embeddings and Latent Topics

Bei Shi, Wai Lam, Shoaib Jameel +2

Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent t…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.