New project: seq2rel

[JohnGiorgi]

Hi! Submitting some of our recent work that used AllenNLP for inclusion in the gallery :)

Project metadata:

{
  "title": "A sequence-to-sequence approach for document-level relation extraction",
  "authors": [
    {
      "name": "John Giorgi",
      "twitter": "@johnmgiorgi",
      "email": "john.giorgi@utoronto.ca",
      "affiliation": "University of Toronto",
      "google_scholar_author_page": "https://scholar.google.ca/citations?user=TNFEhK4AAAAJ&hl=en",
      "s2_author_page": "https://www.semanticscholar.org/author/John-Giorgi/37585306"
    }, {
      "name": "Gary Bader",
      "twitter": "@garybader1",
      "email": "gary.bader@utoronto.ca",
      "affiliation": "University of Toronto",
      "google_scholar_author_page": "https://scholar.google.com/citations?user=22M9eisAAAAJ&hl=en",
    },{
      "name": "Bo Wang",
      "twitter": "@BoWang87",
      "email": "bowang@vectorinstitute.ai",
      "affiliation": "University of Toronto",
      "google_scholar_author_page": "https://scholar.google.com/citations?user=37FDILIAAAAJ&hl=en",
    },
  ],
  "submission_date": "2022-04-03",
  "github_link": "https://github.com/JohnGiorgi/seq2rel",
  "paper_link": "https://aclanthology.org/2022.bionlp-1.2/",
  "allennlp_version": "2.9.0",
  "datasets": [
    {
      "name": "CDR",
      "link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/"
    },
    {
      "name": "GDA",
      "link": "https://link.springer.com/chapter/10.1007/978-3-030-17083-7_17"
    },
    {
      "name": "DGM",
      "link": "https://aclanthology.org/N19-1370/"
    },
    {
      "name": "DocRED",
      "link": "https://aclanthology.org/P19-1074/"
    }
  ],
  "tags": ["named entity recognition", "coreference resolution", "relation extraction", "seq2seq"]
}

Description:

Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training steps. In this paper, we develop a sequence-to-sequence approach, seq2rel, that can learn the subtasks of DocRE (entity extraction, coreference resolution and relation extraction) end-to-end, replacing a pipeline of task-specific components. Using a simple strategy we call entity hinting, we compare our approach to existing pipeline-based methods on several popular biomedical datasets, in some cases exceeding their performance. We also report the first end-to-end results on these datasets for future comparison. Finally, we demonstrate that, under our model, an end-to-end approach outperforms a pipeline-based approach.

[dirkgr]

Sorry for the delay.

[JohnGiorgi]

No worries!!