LUNA.Discourse

LUNA Corpus Discourse Data Set consists of 60 dialogs from Italian LUNA Human-Human Corpus in the hardware/software help desk domain annotated following Penn Discourse Treebank (PDTB) guideline. The data set contains a total of 1,606 discourse relations; 1,052 are explicit discourse relations.

The dialogs are split into training ( section 02), development ( section 01), and test ( section 03) sets as: 42, 6, and 12 respectively.

Data Format

Each dialog (file) is stored as a JSON file that has the following structure:

{
  "DOC_ID": "numeric part of a filename",
  "tokens": "flat list of tokens",
  "blocks": "list of token start & end indices for blocks in text file (tab-separated)",
  "groups": "list of token start & end indices for groups in text file (newline-separated)",
  "relations": "list of discourse relations"
}

For example (reduced):

{
  "DOC_ID": "0703000001",
  "tokens": [
    "helpdesk", "buongiorno", "sono", "<PER>",
    "s\u00ec", "sono", "<PER>", "un", "collega",
    "ho", "il",
    "PC",
    "che", "presumibilmente", "non", "funziona", "da",
    "s\u00ec", "stamattina"
  ],
  "blocks": [[0, 4], [4, 9], [9, 11], [11, 12], [12, 17]],
  "groups": [[0, 4], [4, 17]],
  "relations": [
    {
      "label": "Implicit",
      "sense": "Expansion.Conjunction",
      "conns": "e",
      "conn": [],
      "arg1": [[5, 9]],
      "arg2": [[9, 17], [18, 19]],
      "sup1": [],
      "sup2": []
    },
    {
      "label": "Explicit",
      "sense": "Expansion.Restatement.Equivalence",
      "conn": [[59, 60]],
      "arg1": [[5, 7]],
      "arg2": [[60, 63]],
      "sup1": [],
      "sup2": []
    },
    {
      "label": "AltLex",
      "sense": "Expansion.Restatement",
      "conn": [[159, 161]],
      "arg1": [[141, 144], [151, 154], [169, 171]],
      "arg2": [[157, 164]],
      "sup1": [[137, 141]],
      "sup2": []
    }
  ]
}

Data Schemas

Below are the schemas for a relation and a dialog (in dataclass format).

import typing as t

class DiscourseRelation:
    # label(s)
    label: str  # type
    sense: str  # relation sense
    conns: str  # connective string (for Implicit)
    # spans
    conn: t.List[t.Tuple[int, int]] = None
    arg1: t.List[t.Tuple[int, int]] = None
    arg2: t.List[t.Tuple[int, int]] = None
    sup1: t.List[t.Tuple[int, int]] = None
    sup2: t.List[t.Tuple[int, int]] = None
    

class Dialog:
    doc_id: str
    tokens: t.List[str]
    blocks: t.List[t.Tuple[int, int]]= None
    groups: t.List[t.Tuple[int, int]] = None
    relations: t.List[DiscourseRelation] = None

Spans

A Discourse Relation can contain 5 spans: a discourse connective (conn), its arguments (arg1 and arg2), and supplementary materials to the arguments (sup1 and sup2). Each span can be composed of 0 or more non-adjacent segments. Consequently, all spans are lists of start & end indices with respect to tokens; e.g. [[141, 144], [151, 154], [169, 171]],

LUNA Relation Types (Labels)

Since LUNA is following PDTB format, Discourse Relation types are the same. The distribution is given below.

Type	ALL	TRN	DEV	TST
Explicit	1,052	659	135	258
Implicit	490	294	74	122
AltLex	11	8	2	1
EntRel	56	33	7	16

LUNA Relation Senses

A Discourse Relation can have several senses with respect to the Relation Type:

• Explicit relations can have only 2 senses.
• Implicit relations can have up to 4 senses: 2 connectives with 2 senses each.
• AltLex relations are as Explicit relations.
• EntRel relations have no senses.

The observed sense counts are the following:

• 0 - no sense (errors)
• 1s - 1 sense
• 2s - 2 senses
• 2c - 2 connectives, 1 sense each

Type	ALL	0	1s	2s	2c
Explicit	1,052	4	1,045	3	NA
Implicit	490	3	481	3	3
AltLex	11	1	10	NA	NA
EntRel	56	NA	NA	NA	NA

Relation Sense Selection

Since the amount of discourse relations having a second sense is very little (3 Explicit & 3 Implicit with a second sense and 3 Implicit with a second connective); all the discourse relations have been "simplified" to have exactly 1 sense (or 0, if missing).

In case more than 1 sense is available, the selected sense is the first one. For Implicit 2 connective relations it is the 1st sense of the 1st connective.

Relation Sense Levels

LUNA (and PDTB) Discourse Relations Senses are 3+ level: e.g. Comparison.Concession.Epistemic concession. It is often the case that relations are annotated up to a certain level; i.e. not all relations have all 3 levels.

Level 1 Senses

PDTB has 4 Level 1 senses: Comparison, Contingency, Expansion and Temporal. LUNA adds 3 more which have only 1 level:

• Discourse Marker
• Interrupted
• Repetition

While Interrupted and Repetition senses are quite frequent, Discourse Marker appears only once.

Sense	Explicit	Implicit	AltLex
Comparison	187	47	0
Contingency	462	106	3
Expansion	213	161	4
Temporal	156	64	0
Interrupted	29	1	0
Repetition	0	108	0
Discourse Marker	1	0	0
MISSING	4	3	1

Level 2 Senses

Even though mose relations have level 2 sense, a relation can have a level 1 sense only.

Level 3+ Senses

The 3rd level further categorizes L2 relations into the following types: (as Comparison.Concession.Epistemic concession, Contingency.Cause.Semantic cause, etc.). Refer to Tonelli et al. (2010) for further detail.

• Epistemic
• Inferential
• Pragmatic
• Propositional
• Semantic
• Speech act

Temporal sense has no 3rd level, i.e. only

• Temporal.Asynchronous
• Temporal.Synchrony

Expansion.Restatement on level 3 is further categorized into:

• Expansion.Restatement.Equivalence
• Expansion.Restatement.Specification

Sense Counts

The table below contains sense counts as they appear in the data.

Sense	Explicit	Implicit	AltLex
Comparison (no L2)	1	0	0
Comparison.Concession	144	27	0
Comparison.Contrast	42	20	0
Contingency (no L2)	1	0	0
Contingency.Cause	265	88	2
Contingency.Condition	124	8	1
Contingency.Goal	73	10	1
Expansion (no L2)	1	0	0
Expansion.Alternative	28	3	1
Expansion.Conjunction	111	70	1
Expansion.Instantiation	8	3	1
Expansion.Restatement (no L3)	4	8	1
Expansion.Restatement.Equivalence	25	22	0
Expansion.Restatement.Specification	36	55	2
Temporal (no L2)	0	0	0
Temporal.Asynchronous	128	55	3
Temporal.Synchrony	28	9	3
Interrupted	29	1	0
Repetition	0	108	0
Discourse Marker	1	0	0
MISSING	4	3	1

Anonymization

The data has been anonymized at token-level using the following conversions:

Replacement	Freq	Description
<NUM>	337	number-words; e.g. duomilasei
<ORD>	29	ordinals; e.g. quarto
<DIGIT>	740	digit-words; e.g. due
<CHAR>	86	letter; e.g. C
<PUNC>	18	punctuation; e.g. barra
<WORD>	11	a word to be masked; e.g. password, spelling
<CHARS>	5	a sequence of letters (abbreviation); e.g. SG
<BRAND>	36	brands (hardware); e.g. Fujitsu
<SW>	159	software; e.g. Windows
<PER>	278	person names; e.g. Monica
<ORG>	54	named organizations; e.g. CSI
<LOC>	126	locations; e.g. Italia
<LOC.SPELL>	25	locations for spelling; e.g. Ancona
<WD>	13	week days; e.g. domenica
<MM>	13	month names; e.g. gennaio
<MISC>	2	other; not covered above

Notes, Known Issues, Peculiarities and TODOs

• 0704000020: conn and arg2 spans overlap in Explicit relation (DONE)

• 0 sense relations (8):

  • Relation Types

    • Explicit: 4
    • Implicit: 3
    • AltLex: 1

  • IDs

    • 0703000006: 1
    • 0704000001: 1
    • 0704000025: 1
    • 0704000031: 1
    • 0704000034: 1
    • 0704000051: 2
    • 0705000003: 1

References

If you use this dataset for publication, please cite the following papers:

• Sara Tonelli, Giuseppe Riccardi, Rashmi Prasad, and Aravind K. Joshi, "Annotation of discourse relations for conversational spoken dialogs.", In Proceedings of the International Conference on Language Resources and Evaluation (LREC), 2010.

• Giuseppe Riccardi, Evgeny A. Stepanov, and Shammur Absar Chowdhury. "Discourse connective detection in spoken conversations.", IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016.