Automatic input-grounded text generation tasks process input texts and generate human-understandable natural language text for the processed information. The development
of neural sequence-to-sequence (seq2seq) models, which are usually trained in an end-to-end fashion, pushed the frontier of the performance on text generation tasks expeditiously. However, they are claimed to be defective in semantic consistency w.r.t. their
corresponding input texts. Also, not only the models are to blame. The corpora themselves always include examples whose output is semantically inconsistent to its input.
Any model that is agnostic to such data divergence issues will be prone to semantic inconsistency. Meanwhile, the most widely-used overlap-based evaluation metrics
comparing the generated texts to their corresponding references do not evaluate the
input-output semantic consistency explicitly, which makes this problem hard to detect.
In this thesis, we focus on studying semantic consistency in three automatic text
generation scenarios: Data-to-text Generation, Single Document Abstractive Summarization, and Chit-chat Dialogue Generation, by seeking for the answers to the following research questions: (1) how to define input-output semantic consistency in different
text generation tasks? (2) how to quantitatively evaluate the input-output semantic
consistency? (3) how to achieve better semantic consistency in individual tasks?
We systematically define the semantic inconsistency phenomena in these three
tasks as omission, intrinsic hallucination, and extrinsic hallucination. For Data-to-text Generation, we jointly learn a sentence planner that tightly controls which part
of input source gets generated in what sequence, with a neural seq2seq text generator,
to decrease all three types of semantic inconsistency in model-generated texts. The
evaluation results confirm that the texts generated by our model contain much less
omissions while maintaining low level of extrinsic hallucinations without sacrificing
fluency compared to seq2seq models. For Single Document Abstractive Summarization, we reduce the level of extrinsic hallucinations in training data by automatically
introducing assisting articles to each document-summary instance to provide the supplemental world-knowledge that is present in the summary but missing from the doc ument. With the help of a novel metric, we show that seq2seq models trained with as sisting articles demonstrate less extrinsic hallucinations than the ones trained without
them. For Chit-chat Dialogue Generation, by filtering out the omitted and hallucinated
examples from training set using a newly introduced evaluation metric, and encoding
it into the neural seq2seq response generation models as a control factor, we diminish
the level of omissions and extrinsic hallucinations in the generated dialogue responses
