demo.yaml

defaults:
  - _self_
  # default retriever, see subfolder retriever for more details
  - retriever: related_span_retriever_with_relations_from_other_docs
  # default argumentation model, see subfolder argumentation_model for more details
  - argumentation_model: pipelined_new
  # since this requires a running GROBID server, we disable it by default
  - pdf_fulltext_extractor: none

# Whether to handle segmented entities in the document. If True, labeled_spans are converted
# to labeled_multi_spans and binary_relations with label "parts_of_same" are used to merge them.
# This requires the networkx package to be installed.
handle_parts_of_same: true
# Split the document text into sections that are processed separately.
default_split_regex: "\n\n\n+"

# retriever details (query parameters)
default_min_similarity: 0.95
default_top_k: 10

# data import details
default_arxiv_id: "1706.03762"
default_load_pie_dataset_kwargs:
  path: "pie/sciarg"
  name: "resolve_parts_of_same"
  split: "train"

# set to the data directory of https://github.com/acl-org/acl-anthology
#   to enable ACL venue PDF import (requires to also have a valid pdf_fulltext_extractor)
# acl_anthology_data_dir=../acl-anthology/data
# temporary directory to store downloaded PDFs
acl_anthology_pdf_dir: "data/acl-anthology/pdf"

# for better readability in the UI
render_mode_captions:
  displacy: "displaCy + highlighted arguments"
  pretty_table: "Pretty Table"
layer_caption_mapping:
  labeled_multi_spans: "adus"
  binary_relations: "relations"
  labeled_partitions: "partitions"
relation_name_mapping:
  supports_reversed: "supported by"
  contradicts_reversed: "contradicts"

default_render_mode: "displacy"
default_render_kwargs:
  entity_options:
    # we need to have the keys as uppercase because the spacy rendering function converts the labels to uppercase
    colors:
      OWN_CLAIM: "#009933"
      BACKGROUND_CLAIM: "#99ccff"
      DATA: "#993399"
  colors_hover:
    selected: "#ffa"
    # tail options for relationships
    tail:
      # green
      supports: "#9f9"
      # red
      contradicts: "#f99"
      # do not highlight
      parts_of_same: null
    head: null # "#faf"
    other: null

example_text: >
  Scholarly Argumentation Mining (SAM) has recently gained attention due to its
  potential to help scholars with the rapid growth of published scientific literature.
  It comprises two subtasks: argumentative discourse unit recognition (ADUR) and
  argumentative relation extraction (ARE), both of which are challenging since they
  require e.g. the integration of domain knowledge, the detection of implicit statements,
  and the disambiguation of argument structure.

  While previous work focused on dataset construction and baseline methods for
  specific document sections, such as abstract or results, full-text scholarly argumentation
  mining has seen little progress. In this work, we introduce a sequential pipeline model
  combining ADUR and ARE for full-text SAM, and provide a first analysis of the
  performance of pretrained language models (PLMs) on both subtasks.

  We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best
  reported result by a large margin (+7% F1). We also present the first results for ARE, and
  thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals
  that non-contiguous ADUs as well as the interpretation of discourse connectors pose major
  challenges and that data annotation needs to be more consistent.