  Beam search is an effective and widely used decoding algorithm in many
sequence-to-sequence (seq2seq) text generation tasks. However, in open-ended
text generation, beam search is often found to produce repetitive and generic
texts, sampling-based decoding algorithms like top-k sampling and nucleus
sampling are more preferred. Standard seq2seq models suffer from label bias due
to its locally normalized probability formulation. This paper provides a series
of empirical evidence that label bias is a major reason for such degenerate
behaviors of beam search. By combining locally normalized maximum likelihood
estimation and globally normalized sequence-level training, label bias can be
reduced with almost no sacrifice in perplexity. To quantitatively measure label
bias, we test the model's ability to discriminate the groundtruth text and a
set of context-agnostic distractors. We conduct experiments on large-scale
response generation datasets. Results show that beam search can produce more
diverse and meaningful texts with our approach, in terms of both automatic and
human evaluation metrics. Our analysis also suggests several future working
directions towards the grand challenge of open-ended text generation.
