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A.1 Definitions of New Relation Types
We follow the original guideline to annotate relation types in the TAC-KBP SF task (marked with  $\star$ ) unless stated otherwise and define new relation types as follows except for self-explainable ones (e.g., PER:MAJOR, PER:FRIENDS, and PER:CLIENT). In this section, we keep the original speaker names in examples for better readability.
o per:alternate_names\*: Names used to refer a person that are distinct from speaker names or the first name mention in the given dialogue. It is possible to provide correct objects for this relation type without any contextual information such as triggers. Alternate names may include nicknames, first name, aliases, stage names, alternate transliterations, abbreviations, alternate spellings, full names, and birth names. However, if the full name mention appears first, we do not regard a first/last name alone as a valid value. An alternate name can also be a single word or a noun phrase.
○ per:positive_impression: Have a positive impression (psychological) towards an object (e.g., a person, a book, a team, a song, a shop, or location). A named entity is expected here.
○ per: negative impression: Have a negative impression (psychological) towards an object. A named entity is expected here.
○ per:acquaintance: A person one knows slightly (e.g., name), but who is not a close friend.
○ per:alumni: Two persons studied in the same school, college, or university, not necessarily during the same period. Two persons can be in different majors. Classmates or classmates also belong to this relation type.
o per:boss: In most cases, we annotate B as the boss of A when A directly reports to B and is managed by B at work. In the meantime, A is the subordinate of B. For example, we label ("Rachel", per:boss, "Joanna") and its corresponding trigger "assistant" based on dialogue D1.
Rachel: Oh, uh, Joanna I was wondering if I could ask you something. There's an opening for an assistant buyer in Junior Miss...
Joanna: Okay, but that would actually be a big step down for me.
Rachel: Well, actually, I meant for me. The hiring committee is meeting people all day and...
Joanna: Oh. Well, I wish I could say no, but you cant stay my assistant forever. Neither can you Sophie, but for different reasons.
per:girl/boyfriend: A relatively long-standing relationship compared to PER:POSITIVE_IMPRESSION and PER:DATES, including but not limited to ex-relationships, partners, and engagement. The fact that two people dated for one or several times alone cannot guarantee that there exists a PER:GIRL/BOYFRIEND relation between them; we label PER:DATES for such an argument pair, instead.
○ per:neighbor: A neighbor could be a person who lives in your apartment building whether they are next door to you, or not. A neighbor could also be in the broader sense of a person who lives in your neighborhood.
o per:roommate: We regard that two persons are roommates if they share a living facility (e.g., an apartment or dormitory), and they are not family or romantically involved (e.g., per:spouse and per:girl/boyfriend).
○ per:visited_place: A person visits a place in a relatively short term of period (vs. PER:PLACE_OF_RESIDENCE). For example, we annotate ("Mike", per:visited_place, "Barbados") in dialogue D2 and its corresponding trigger "coming to".
Phoebe: Okay, not a fan of the tough love.
Precious: I just can't believe that Mike didn't give me any warning.
Phoebe: But he didn't really know, you know. He wasn't planning on coming to Barbados and proposing to me...
Precious: He proposed to you? This is the worst birthday ever.
- per:works: The argument can be a piece of art, a song, a movie, a book, or a TV series.
○ per:place_of_work: A location in the form of a string or a general noun phrase, where a person works such as "shop".
o per:pet: We prefer to use named entities as arguments. If there is no name associated with a pet, we keep its species (e.g., dog) mentioned in a dialogue.
A.2 Relation Type Distribution
A.3 Distance Between Argument Pairs
A.4 Other Input Sequences
We also experiment with the following three alternative input sequences on the BERT baseline: (1) [CLS]  $d^{\#}$  [SEP], (2)
[CLS]  $d^{\#}$  [SEP]  $a_1$  [SEP]  $a_2$  [SEP], and (3)
[CLS]  $d''[\mathrm{SEP}]$ , where  $d^{\#}$  is obtained by
Figure 1: Relation type distribution in DialogRE.
Figure 2: Relation type distribution in SF (2013-2014).
Figure 3: Number of words between two arguments within a dialogue in DialogRE.
replacing subject/object mentions in  $d$  with special tokens [SUBJ] and [OBJ], and  $d''$  is obtained by surrounding each mention of  $a_{i}$ $(i\in \{1,2\})$  in  $d$  with special tokens  $[\mathbb{A}_{\dot{\mathbf{i}}}]$  and  $\left[\mathbb{/A_i}\right]$  (Baldini Soares et al., 2019). The F1 of them is  $50.9\%$ $58.8\%$  ,and  $57.9\%$  ,respectively, substantially lower than that of BERTs  $(61.2\%)$
A.5 Ground Truth Argument Type
Method 3 follows the input sequence employed by Joshi et al. (2020). Specifically, we replace the argument mentions in document  $d$  with newly-introduced special tokens that represent the subject/object and argument types. For example, if the subject type is PER and the object is STRING, we replace every subject mention in  $d$  with [SUBJ-PER] and every object mention with [OBJ-STRING]. Let  $d'$  denote the new document. The input sequence is  $[\mathrm{CLS}]d'[\mathrm{SEP}]$ .
Method 4 takes as input the sequence employed by Shi and Lin (2019). The input sequence is [CLS]  $d^{\prime}$  [SEP]  $a_1$  [SEP]  $a_2$  [SEP], where  $d^{\prime}$  is defined in Method 3.