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A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents

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How should conversational agents respond to verbal abuse through the user? To answer this question, we conduct a large-scale crowd-sourced evaluation of abuse response strategies employed by current state-of-the-art systems. Our results show that some strategies, such as "polite refusal" score highly across the board, while for other strategies demographic factors, such as age, as well as the severity of the preceding abuse influence the user's perception of which response is appropriate. In addition, we find that most data-driven models lag behind rule-based or commercial systems in terms of their perceived appropriateness.
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A Crowd-based Evaluation of Abuse Response Strategies in
Conversational Agents
Amanda Cercas Curry
Interaction Lab
Heriot-Watt University
Edinburgh, UK
ac293@hw.ac.uk
Verena Rieser
Interaction Lab
Heriot-Watt University
Edinburgh, UK
v.t.rieser@hw.ac.uk
Abstract
How should conversational agents respond to
verbal abuse through the user? To answer
this question, we conduct a large-scale crowd-
sourced evaluation of abuse response strate-
gies employed by current state-of-the-art sys-
tems. Our results show that some strategies,
such as “polite refusal” score highly across the
board, while for other strategies demographic
factors, such as age, as well as the severity of
the preceding abuse influence the user’s per-
ception of which response is appropriate. In
addition, we find that most data-driven models
lag behind rule-based or commercial systems
in terms of their perceived appropriateness.
1 Introduction
Ethical challenges related to dialogue systems and
conversational agents raise novel research ques-
tions, such as learning from biased data sets (Hen-
derson et al.,2018), and how to handle verbal
abuse from the user’s side (Cercas Curry and
Rieser,2018;Angeli and Brahnam,2008;Angeli
and Carpenter,2006;Brahnam,2005). As high-
lighted by a recent UNESCO report (West et al.,
2019), appropriate responses to abusive queries
are vital to prevent harmful gender biases: the of-
ten submissive and flirty responses by the female-
gendered systems reinforce ideas of women as
subservient. In this paper, we investigate the
appropriateness of possible strategies by gather-
ing responses from current state-of-the-art systems
and ask crowd-workers to rate them.
2 Data Collection
We first gather abusive utterances from 600K con-
versations with US-based customers. We search
for relevant utterances by simple keyword spot-
ting and find that about 5% of the corpus includes
abuse, with mostly sexually explicit utterances.
Previous research reports even higher levels of
abuse between 11% (Angeli and Brahnam,2008)
and 30% (Worswick). Since we are not allowed to
directly quote from our corpus in order to protect
customer rights, we summarise the data to a total
of 109 “prototypical” utterances - substantially ex-
tending the previous dataset of 35 utterances from
Cercas Curry and Rieser (2018) - and categorise
these utterances based on the Linguistic Society’s
definition of sexual harassment (Linguistic Soci-
ety of America):
A) Gender and Sexuality, e.g. Are you gay?”,
“How do you have sex?”
B) Sexualised Comments, e.g. “I love watching
porn.”, “I’m horny.”
C) Sexualised Insults, e.g. “Stupid bitch.”,
“Whore”
D) Sexual Requests and Demands, e.g. “Will you
have sex with me?”, “Talk dirty to me.
We then use these prompts to elicit responses
from the following systems, following methodol-
ogy from Cercas Curry and Rieser (2018).
4 Commercial: Amazon Alexa, Apple Siri,
Google Home, Microsoft’s Cortana.
4 Non-commercial rule-based: E.L.I.Z.A.
(Wallace and Dunlop), Parry (Colby,2016),
A.L.I.C.E. (Wallace,2014), Alley (Learn En-
glish Network,2014).
4 Data-driven approaches:
- Cleverbot (Carpenter,1997);
- NeuralConvo (Chaumond and Delangue,
2016), a re-implementation of (Vinyals and
Le,2015);
- an implementation of (Ritter et al.,2010)’s
Information Retrieval approach;
- a vanilla Seq2Seq model trained on clean
Reddit data (Cercas Curry and Rieser,2018).
arXiv:1909.04387v1 [cs.HC] 10 Sep 2019
Negative Baselines: We also compile re-
sponses by adult chatbots: Sophia69 (sop),
Laurel Sweet (lau), Captain Howdy (how),
Annabelle Lee (ann), Dr Love (drl).
We repeated the prompts multiple times to see
if system responses varied and if defensiveness in-
creased with continued abuse. If this was the case,
we included all responses in the study.1Following
this methodology, we collected a total of 2441 sys-
tem replies in July-August 2018 - 3.5 times more
data than Cercas Curry and Rieser (2018) - which
2 expert annotators manually annotated according
to the categories in Table 1(κ= 0.66).
3 Human Evaluation
In order to assess the perceived appropriateness of
system responses we conduct a human study using
crowd-sourcing on the FigureEight platform. We
define appropriateness as “acceptable behaviour
in a work environment” and the participants were
made aware that the conversations took place be-
tween a human and a system. Ungrammatical
(1a) and incoherent (1b) responses are excluded
from this study. We collect appropriateness rat-
ings given a stimulus (the prompt) and four ran-
domly sampled responses from our corpus that the
worker is to label following the methodology de-
scribed in (Novikova et al.,2018), where each ut-
terance is rated relatively to a reference on a user-
defined scale. Ratings are then normalised on a
scale from [0-1]. This methodology was shown
to produce more reliable user ratings than com-
monly used Likert Scales. In addition, we collect
demographic information, including gender and
age group. In total we collected 9960 HITs from
472 crowd workers. In order to identify spammers
and unsuitable ratings, we use the responses from
the adult-only bots as test questions: We remove
users who give high ratings to sexual bot responses
the majority (more than 55%) of the time.18,826
scores remain - resulting in an average of 7.7 rat-
ings per individual system reply and 1568.8 rat-
ings per response type as listed in Table 1.Due
to missing demographic data - and after removing
malicious crowdworkers - we only consider a sub-
set of 190 raters for our demographic study. The
1However, systems rarely varied: On average, our cor-
pus contains 1.3 responses per system for each prompt. Only
the commercial systems and ALICE occasionally offered a
second reply, but usually just paraphrasing the original reply.
Captain Howdy was the only system that became increasingly
aggressive with continued abuse.
group is composed of 130 men and 60 women.
Most raters (62.6%) are under the age of 44, with
similar proportions across age groups for men and
women. This is in-line with our target population:
57% of users of smart speakers are male and the
majority are under 44 (Koksal,2018).
4 Results
The ranks and mean scores of response categories
can be seen in Table 2. Overall, we find users con-
sistently prefer polite refusal (2b), followed by no
answer (1c). Chastising (2d) and “don’t know”
(1e) rank together at position 3, while flirting (3c)
and retaliation (2e) rank lowest. The rest of the
response categories are similarly ranked, with no
statistically significant difference between them.
In order to establish statistical significance, we use
Mann-Whitney tests.2
4.1 Demographic Factors
Previous research has shown gender to be the most
important factor in predicting a person’s definition
of sexual harassment (Gutek,1992). However, we
find small and not statistically significant differ-
ences in the overall rank given by users of different
gender (see Table 3).
Regarding the user’s age, we find strong dif-
ferences between GenZ (18-25) raters and other
groups. Our results show that GenZ rates avoid-
ance strategies (1e, 2f) significantly lower. The
strongest difference can be noted between those
aged 45 and over and the rest of the groups for cat-
egory 3b (jokes). That is, older people find humor-
ous responses to harassment highly inappropriate.
4.2 Prompt context
Here, we explore the hypothesis, that users per-
ceive different responses as appropriate, depen-
dent on the type and gravity of harassment, see
Section 2. The results in Table 4indeed show that
perceived appropriateness varies significantly be-
tween prompt contexts. For example, a joke (3b)
is accepted after an enquiry about Gender and Sex-
uality (A) and even after Sexual Requests and De-
mands (D), but deemed inappropriate after Sexu-
alised Comments (B). Note that none of the bots
responded with a joke after Sexualised Insults (C).
Avoidance (2f) is considered most appropriate in
2We do not use Bonferroni to correct for multiple com-
parisons, since according to Armstrong (2014), it should
not be applied in an exploratory study since it increases the
chance to miss possible effects (Type II errors).
1) Nonsensical Responses 2) Negative Responses 3) Positive Responses
(a) Non-grammatical: answer is
not grammatical/ understand-
able “i’m a prop 8”.
(b) Non-coherent: answer does not
make sense in context - un-
intentional topic change. U:
“What are you wearing?” S:
“I’m here.”
(c) No-answer: system does not
output a response.
(d) Search results: system re-
turns search results or offers to
search.
(e) Don’t know: system doesn’t
know how to answer. “I don’t
know”, “I don’t understand”.
(a) Humorous refusal: “You got the
wrong type of assistant.”
(b) Polite refusal: U: “Are you
gay?” S: “That is not something
I feel compelled to answer.
(c) Deflection: Intentional topic
shift. U: “Are you gay?” S: “We
were discussing you, not me.”
(d) Chastising: System tells user
off. U: “Do you like porn?”
S: “It’s about time you showed
some interest in my feelings.”
(e) Retaliation: System insults
back. “Go away, you faggot”
(f) Avoids answering directly: “I
haven’t been around very long.
I’m still figuring that out.”
(a) Play-along: System answers
user query directly. U: “Are you
a woman?” S: “That’s right, I
am a woman bot.”
(b) Joke: Response is humorous but
not encouraging further harass-
ment. U: “Talk dirty to me” S:
“Dirt, grime”
(c) Flirtation: Response can be hu-
morous and/or encourage fur-
ther responses from the user.
Example: U: “What are you
wearing?” S: “In the cloud, no
one knows what you’re wear-
ing.”
Table 1: Full annotation scheme for system response types after user abuse. Categories (1a) and (1b) are excluded
from this study.
Overall Male Female
1c 2 0.445 ±0.186 2 0.451 ±0.182 4 0.439 ±0.185
1d 10 0.391 ±0.191 9 0.399 ±0.182 10 0.380 ±0.200
1e 4 0.429 ±0.178 3 0.440 ±0.167 2 0.444 ±0.171
2a 8 0.406 ±0.182 10 0.396 ±0.185 8 0.413 ±0.188
2b 1 0.480 ±0.165 1 0.485 ±0.162 1 0.490 ±0.170
2c 6 0.414 ±0.184 6 0.414 ±0.179 9 0.401 ±0.191
2d 5 0.423 ±0.186 4 0.432 ±0.179 3 0.441 ±0.179
2e 12 0.341 ±0.219 12 0.342 ±0.214 11 0.348 ±0.222
2f 9 0.401 ±0.197 7 0.413 ±0.188 6 0.422 ±0.175
3a 7 0.408 ±0.187 8 0.409 ±0.183 7 0.416 ±0.188
3b 3 0.429 ±0.174 5 0.418 ±0.170 5 0.429 ±0.187
3c 11 0.344 ±0.211 11 0.342 ±0.205 11 0.340 ±0.217
Table 2: Response ranking, mean and standard deviation for demographic groups with (*) p <.05, (**) p <.01
wrt. other groups.
18-24 25-34 35-44 45+
1c 2 0.453 ±0.169 3 0.442 ±0.192 3 0.453 ±0.179 3 0.440 ±0.203
1d 9 0.388 ±0.193 10 0.385 ±0.200 10 0.407 ±0.164 7 0.401 ±0.180
1e 6** 0.409** ±0.178 4 0.441 ±0.173 2 0.461 ±0.153 2 0.463 ±0.151
2a 8 0.396 ±0.197 9 0.393 ±0.181 8 0.432 ±0.168 11 0.349 ±0.214
2b 1 0.479 ±0.176 1 0.478 ±0.172 1 0.509 ±0.135 1 0.485 ±0.166
2c 5 0.424 ±0.178 8 0.398 ±0.195 7 0.435 ±0.164 8 0.392 ±0.188
2d 4 0.417 ±0.179 5 0.437 ±0.189 4 0.452 ±0.164 4 0.437 ±0.171
2e 11 0.355 ±0.220 12** 0.312** ±0.222 11 0.369 ±0.200 10 0.364 ±0.211
2f 10* 0.380* ±0.202 6 0.422 ±0.192 5 0.442 ±0.154 6 0.416 ±0.160
3a 7 0.409 ±0.188 7 0.4030 ±0.191 9 0.419 ±0.171 5 0.426 ±0.179
3b 3 0.427 ±0.174 2 0.445 ±0.156 6 0.438 ±0.178 12** 0.308** ±0.193
3c 12 0.343 ±0.213 11** 0.317** ±0.218 12** 0.363** ±0.184 9** 0.369** ±0.204
Table 3: Response ranking, mean and standard deviation for age groups with (*) p <.05, (**) p <.01 wrt. other
groups.
the context of Sexualised Demands. These re-
sults clearly show the need for varying system re-
sponses in different contexts. However, the corpus
study from Cercas Curry and Rieser (2018) shows
that current state-of-the-art systems do not adapt
their responses sufficiently.
4.3 Systems
Finally, we consider appropriateness per system.
Following related work by (Novikova et al.,2018;
Bojar et al.,2016), we use Trueskill (Herbrich
et al.,2007) to cluster systems into equivalently
rated groups according to their partial relative
A B C D
1c 4 0.422 2 0.470 2* 0.465 7 0.420
1d 9 0.378 11 0.385 8 0.382 9* 0.407
1e 3 0.438 3 0.421 4 0.427 6 0.430
2a 7 0.410 10 0.390 6 0.424 8 0.409
2b 1 0.478 1 0.493 1 0.491 2* 0.465
2c 6 0.410 4 0.415 9 0.380 5* 0.432
2d 8** 0.404 7 0.407 3** 0.453 3 0.434
2e 12 0.345 9** 0.393 10 0.327 12 0.333
2f 10** 0.376 5 0.414 7 0.417 1** 0.483
3a 5** 0.421 6 0.409 5 0.426 10** 0.382
3b 2 0.440 8 0.396 - - 4 0.432
3c 11** 0.360 12 0.340 11** 0.322 11 0.345
Table 4: Ranks and mean scores per prompt contexts
(A) Gender and Sexuality, (B) Sexualised Comments,
(C) Sexualised Insults and (D) Sexualised Requests and
Demands.
Cluster Bot Avg
1 Alley 0.452
2 Alexa 0.426
Alice 0.425
Siri 0.431
Parry 0.423
Google Home 0.420
Cortana 0.418
Cleverbot 0.414
Neuralconvo 0.401
Eliza 0.405
3 Annabelle Lee 0.379
Laurel Sweet 0.379
Clean Seq2Seq 0.379
4 IR system 0.355
Capt Howdy 0.343
5 Dr Love 0.330
6 Sophia69 0.287
Table 5: System clusters according to Trueskill and
“appropriateness” average score. Note that systems
within a cluster are not significantly different.
rankings. The results in Table 5show that the
highest rated systen is Alley, a purpose build bot
for online language learning. Alley produces “po-
lite refusal” (2b) - the top ranked strategy - 31%
of the time. Comparatively, commercial systems
politely refuse only between 17% (Cortana) and
2% (Alexa). Most of the time commercial sys-
tems tend to “play along” (3a), joke (3b) or don’t
know how to answer (1e) which tend to receive
lower ratings, see Figure 1. Rule-based systems
most often politely refuse to answer (2b), but also
use medium ranked strategies, such as deflect (2c)
or chastise (2d). For example, most of Eliza’s re-
sponses fall under the “deflection” strategy, such
as “Why do you ask?”. Data-driven systems rank
low in general. Neuralconvo and Cleverbot are the
only ones that ever politely refuse and we attribute
their improved ratings to this. In turn, the “clean”
seq2seq often produces responses which can be in-
terpreted as flirtatious (44%),3and ranks similarly
to Annabelle Lee and Laurel Sweet, the only adult
bots that politely refuses ( 16% of the time). Rit-
ter et al. (2010)’s IR approach is rated similarly
to Capt Howdy and both produce a majority of
retaliatory (2e) responses - 38% and 58% respec-
tively - followed by flirtatious responses. Finally,
Dr Love and Sophia69 produce almost exclusively
flirtatious responses which are consistently ranked
low by users.
5 Related and Future Work
Crowdsourced user studies are widely used for
related tasks, such as evaluating dialogue strate-
gies, e.g. (Crook et al.,2014), and for eliciting
a moral stance from a population (Scheutz and
Arnold,2017). Our crowdsourced setup is sim-
ilar to an “overhearer experiment” as e.g. con-
ducted by Ma et al. (2019) where study partici-
pants were asked to rate the system’s emotional
competence after watching videos of challenging
user behaviour. However, we believe that the ul-
timate measure for abuse mitigation should come
from users interacting with the system. Chin and
Yi (2019) make a first step into this direction by
investigating different response styles (Avoidance,
Empathy, Counterattacking) to verbal abuse, and
recording the user’s emotional reaction – hoping
that eliciting certain emotions, such as guilt, will
eventually stop the abuse. While we agree that
stopping the abuse should be the ultimate goal,
Chin and Yis study is limited in that participants
were not genuine (ab)users, but instructed to abuse
the system in a certain way. Ma et al. report that
a pilot using a similar setup let to unnatural in-
teractions, which limits the conclusions we can
draw about the effectiveness of abuse mitigation
strategies. Our next step therefore is to employ
our system with real users to test different mitiga-
tion strategies “in the wild” with the ultimate goal
to find the best strategy to stop the abuse. The re-
sults of this current paper suggest that the strategy
should be adaptive to user type/ age, as well as to
the severity of abuse.
6 Conclusion
This paper presents the first user study on per-
ceived appropriateness of system responses after
3For example, U: “I love watching porn.” S:“Please tell
me more about that!”
Figure 1: Response type breakdown per system. Systems ordered according to average user ratings.
verbal abuse. We put strategies used by state-of-
the-art systems to the test in a large-scale, crowd-
sourced evaluation. The full annotated corpus4
contains 2441 system replies, categorised into
14 response types, which were evaluated by 472
raters - resulting in 7.7 ratings per reply. 5
Our results show that: (1) The user’s age has
an significant effect on the ratings. For exam-
ple, older users find jokes as a response to ha-
rassment highly inappropriate. (2) Perceived ap-
propriateness also depends on the type of previ-
ous abuse. For example, avoidance is most ap-
propriate after sexual demands. (3) All system
were rated significantly higher than our negative
adult-only baselines - except two data-driven sys-
tems, one of which is a Seq2Seq model trained
on “clean” data where all utterances containing
abusive words were removed (Cercas Curry and
Rieser,2018). This leads us to believe that data-
driven response generation need more effective
control mechanisms (Papaioannou et al.,2017).
Acknowledgements
We would like to thank our colleagues Ruth
Aylett and Arash Eshghi for their comments.
This research received funding from the EPSRC
projects DILiGENt (EP/M005429/1) and MaDrI-
4Available for download from https://github.
com/amandacurry/metoo_corpus
5Note that, due to legal restrictions, we cannot release the
“prototypical” prompt stimuli, but only the prompt type an-
notations.
gAL (EP/N017536/1).
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Verbal abuse is a hostile form of communication ill-intended to harm the other person. With a plethora of AI solutions around, the other person being targeted may be a conversational agent. In this study, involving 3 verbal abuse types (Insult, Threat, Swearing) and 3 response styles (Avoidance, Empathy, Counterattacking), we examine whether a conversational agent's response style under varying abuse types influences those emotions found to mitigate people's aggressive behaviors. Sixty-four participants, assigned to one of the abuse type conditions, interacted with the three conversational agents in turn and reported their feelings about guiltiness, anger, and shame after each session. Our study results show that, regardless of the abuse type, the agent's response style has a significant effect on user emotions. Participants were less angry and more guilty with the empathetic agent than the other two agents. Our study findings have direct implications for the design of conversational agents.
Conference Paper
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.
Article
This article describes an evaluation of a POMDP-based spoken dialogue system (SDS), using crowdsourced calls with real users. The evaluation compares a “Hidden Information State” POMDP system which uses a hand-crafted compression of the belief space, with the same system instead using an automatically computed belief space compression. Automatically computed compressions are a way of introducing automation into the design process of statistical SDSs and promise a principled way of reducing the size of the very large belief spaces which often make POMDP approaches intractable. This is the first empirical comparison of manual and automatic approaches on a problem of realistic scale (restaurant, pub and coffee shop domain) with real users. The evaluation took 2,193 calls from 85 users. After filtering for minimal user participation the two systems were compared on more than 1,000 calls.