46 research outputs found
RankME: Reliable Human Ratings for Natural Language Generation
Human evaluation for natural language generation (NLG) often suffers from
inconsistent user ratings. While previous research tends to attribute this
problem to individual user preferences, we show that the quality of human
judgements can also be improved by experimental design. We present a novel
rank-based magnitude estimation method (RankME), which combines the use of
continuous scales and relative assessments. We show that RankME significantly
improves the reliability and consistency of human ratings compared to
traditional evaluation methods. In addition, we show that it is possible to
evaluate NLG systems according to multiple, distinct criteria, which is
important for error analysis. Finally, we demonstrate that RankME, in
combination with Bayesian estimation of system quality, is a cost-effective
alternative for ranking multiple NLG systems.Comment: Accepted to NAACL 2018 (The 2018 Conference of the North American
Chapter of the Association for Computational Linguistics
Findings of the E2E NLG Challenge
This paper summarises the experimental setup and results of the first shared
task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue
systems. Recent end-to-end generation systems are promising since they reduce
the need for data annotation. However, they are currently limited to small,
delexicalised datasets. The E2E NLG shared task aims to assess whether these
novel approaches can generate better-quality output by learning from a dataset
containing higher lexical richness, syntactic complexity and diverse discourse
phenomena. We compare 62 systems submitted by 17 institutions, covering a wide
range of approaches, including machine learning architectures -- with the
majority implementing sequence-to-sequence models (seq2seq) -- as well as
systems based on grammatical rules and templates.Comment: Accepted to INLG 201
Data-driven Natural Language Generation: Paving the Road to Success
We argue that there are currently two major bottlenecks to the commercial use
of statistical machine learning approaches for natural language generation
(NLG): (a) The lack of reliable automatic evaluation metrics for NLG, and (b)
The scarcity of high quality in-domain corpora. We address the first problem by
thoroughly analysing current evaluation metrics and motivating the need for a
new, more reliable metric. The second problem is addressed by presenting a
novel framework for developing and evaluating a high quality corpus for NLG
training.Comment: WiNLP workshop at ACL 201
Crowd-sourcing NLG Data: Pictures Elicit Better Data
Recent advances in corpus-based Natural Language Generation (NLG) hold the
promise of being easily portable across domains, but require costly training
data, consisting of meaning representations (MRs) paired with Natural Language
(NL) utterances. In this work, we propose a novel framework for crowdsourcing
high quality NLG training data, using automatic quality control measures and
evaluating different MRs with which to elicit data. We show that pictorial MRs
result in better NL data being collected than logic-based MRs: utterances
elicited by pictorial MRs are judged as significantly more natural, more
informative, and better phrased, with a significant increase in average quality
ratings (around 0.5 points on a 6-point scale), compared to using the logical
MRs. As the MR becomes more complex, the benefits of pictorial stimuli
increase. The collected data will be released as part of this submission.Comment: The 9th International Natural Language Generation conference INLG,
2016. 10 pages, 2 figures, 3 table
The E2E Dataset: New Challenges For End-to-End Generation
This paper describes the E2E data, a new dataset for training end-to-end,
data-driven natural language generation systems in the restaurant domain, which
is ten times bigger than existing, frequently used datasets in this area. The
E2E dataset poses new challenges: (1) its human reference texts show more
lexical richness and syntactic variation, including discourse phenomena; (2)
generating from this set requires content selection. As such, learning from
this dataset promises more natural, varied and less template-like system
utterances. We also establish a baseline on this dataset, which illustrates
some of the difficulties associated with this data.Comment: Accepted as a short paper for SIGDIAL 2017 (final submission
including supplementary material
Referenceless Quality Estimation for Natural Language Generation
Traditional automatic evaluation measures for natural language generation
(NLG) use costly human-authored references to estimate the quality of a system
output. In this paper, we propose a referenceless quality estimation (QE)
approach based on recurrent neural networks, which predicts a quality score for
a NLG system output by comparing it to the source meaning representation only.
Our method outperforms traditional metrics and a constant baseline in most
respects; we also show that synthetic data helps to increase correlation
results by 21% compared to the base system. Our results are comparable to
results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate
Natural Language (LGNL), Sydney, 10 August 201
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table
Why We Need New Evaluation Metrics for NLG
The majority of NLG evaluation relies on automatic metrics, such as BLEU . In
this paper, we motivate the need for novel, system- and data-independent
automatic evaluation methods: We investigate a wide range of metrics, including
state-of-the-art word-based and novel grammar-based ones, and demonstrate that
they only weakly reflect human judgements of system outputs as generated by
data-driven, end-to-end NLG. We also show that metric performance is data- and
system-specific. Nevertheless, our results also suggest that automatic metrics
perform reliably at system-level and can support system development by finding
cases where a system performs poorly.Comment: accepted to EMNLP 201